{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 1、加载数据"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "ename": "UnicodeDecodeError",
     "evalue": "'utf-8' codec can't decode byte 0xb0 in position 0: invalid start byte",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mUnicodeDecodeError\u001b[0m                        Traceback (most recent call last)",
      "\u001b[1;32mpandas\\_libs\\parsers.pyx\u001b[0m in \u001b[0;36mpandas._libs.parsers.TextReader._convert_tokens\u001b[1;34m()\u001b[0m\n",
      "\u001b[1;32mpandas\\_libs\\parsers.pyx\u001b[0m in \u001b[0;36mpandas._libs.parsers.TextReader._convert_with_dtype\u001b[1;34m()\u001b[0m\n",
      "\u001b[1;32mpandas\\_libs\\parsers.pyx\u001b[0m in \u001b[0;36mpandas._libs.parsers.TextReader._string_convert\u001b[1;34m()\u001b[0m\n",
      "\u001b[1;32mpandas\\_libs\\parsers.pyx\u001b[0m in \u001b[0;36mpandas._libs.parsers._string_box_utf8\u001b[1;34m()\u001b[0m\n",
      "\u001b[1;31mUnicodeDecodeError\u001b[0m: 'utf-8' codec can't decode byte 0xb0 in position 0: invalid start byte",
      "\nDuring handling of the above exception, another exception occurred:\n",
      "\u001b[1;31mUnicodeDecodeError\u001b[0m                        Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-1-7c4f5d28cb02>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mpandas\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0mpd\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 2\u001b[1;33m \u001b[0mcar_data\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mpd\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mread_csv\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"second_cars_info.csv\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      3\u001b[0m \u001b[0mcar_data\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mD:\\Program\\anaconda3\\lib\\site-packages\\pandas\\io\\parsers.py\u001b[0m in \u001b[0;36mread_csv\u001b[1;34m(filepath_or_buffer, sep, delimiter, header, names, index_col, usecols, squeeze, prefix, mangle_dupe_cols, dtype, engine, converters, true_values, false_values, skipinitialspace, skiprows, skipfooter, nrows, na_values, keep_default_na, na_filter, verbose, skip_blank_lines, parse_dates, infer_datetime_format, keep_date_col, date_parser, dayfirst, cache_dates, iterator, chunksize, compression, thousands, decimal, lineterminator, quotechar, quoting, doublequote, escapechar, comment, encoding, dialect, error_bad_lines, warn_bad_lines, delim_whitespace, low_memory, memory_map, float_precision)\u001b[0m\n\u001b[0;32m    684\u001b[0m     )\n\u001b[0;32m    685\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 686\u001b[1;33m     \u001b[1;32mreturn\u001b[0m \u001b[0m_read\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfilepath_or_buffer\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mkwds\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    687\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    688\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mD:\\Program\\anaconda3\\lib\\site-packages\\pandas\\io\\parsers.py\u001b[0m in \u001b[0;36m_read\u001b[1;34m(filepath_or_buffer, kwds)\u001b[0m\n\u001b[0;32m    456\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    457\u001b[0m     \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 458\u001b[1;33m         \u001b[0mdata\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mparser\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mread\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mnrows\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    459\u001b[0m     \u001b[1;32mfinally\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    460\u001b[0m         \u001b[0mparser\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mclose\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mD:\\Program\\anaconda3\\lib\\site-packages\\pandas\\io\\parsers.py\u001b[0m in \u001b[0;36mread\u001b[1;34m(self, nrows)\u001b[0m\n\u001b[0;32m   1194\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0mread\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mnrows\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mNone\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1195\u001b[0m         \u001b[0mnrows\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0m_validate_integer\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"nrows\"\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mnrows\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1196\u001b[1;33m         \u001b[0mret\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mread\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mnrows\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1197\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1198\u001b[0m         \u001b[1;31m# May alter columns / col_dict\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mD:\\Program\\anaconda3\\lib\\site-packages\\pandas\\io\\parsers.py\u001b[0m in \u001b[0;36mread\u001b[1;34m(self, nrows)\u001b[0m\n\u001b[0;32m   2153\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0mread\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mnrows\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mNone\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2154\u001b[0m         \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 2155\u001b[1;33m             \u001b[0mdata\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_reader\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mread\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mnrows\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   2156\u001b[0m         \u001b[1;32mexcept\u001b[0m \u001b[0mStopIteration\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2157\u001b[0m             \u001b[1;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_first_chunk\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mpandas\\_libs\\parsers.pyx\u001b[0m in \u001b[0;36mpandas._libs.parsers.TextReader.read\u001b[1;34m()\u001b[0m\n",
      "\u001b[1;32mpandas\\_libs\\parsers.pyx\u001b[0m in \u001b[0;36mpandas._libs.parsers.TextReader._read_low_memory\u001b[1;34m()\u001b[0m\n",
      "\u001b[1;32mpandas\\_libs\\parsers.pyx\u001b[0m in \u001b[0;36mpandas._libs.parsers.TextReader._read_rows\u001b[1;34m()\u001b[0m\n",
      "\u001b[1;32mpandas\\_libs\\parsers.pyx\u001b[0m in \u001b[0;36mpandas._libs.parsers.TextReader._convert_column_data\u001b[1;34m()\u001b[0m\n",
      "\u001b[1;32mpandas\\_libs\\parsers.pyx\u001b[0m in \u001b[0;36mpandas._libs.parsers.TextReader._convert_tokens\u001b[1;34m()\u001b[0m\n",
      "\u001b[1;32mpandas\\_libs\\parsers.pyx\u001b[0m in \u001b[0;36mpandas._libs.parsers.TextReader._convert_with_dtype\u001b[1;34m()\u001b[0m\n",
      "\u001b[1;32mpandas\\_libs\\parsers.pyx\u001b[0m in \u001b[0;36mpandas._libs.parsers.TextReader._string_convert\u001b[1;34m()\u001b[0m\n",
      "\u001b[1;32mpandas\\_libs\\parsers.pyx\u001b[0m in \u001b[0;36mpandas._libs.parsers._string_box_utf8\u001b[1;34m()\u001b[0m\n",
      "\u001b[1;31mUnicodeDecodeError\u001b[0m: 'utf-8' codec can't decode byte 0xb0 in position 0: invalid start byte"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "car_data = pd.read_csv(\"second_cars_info.csv\")\n",
    "car_data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 2、数据清洗"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.00824395000443223"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "N = car_data[car_data.Boarding_time == \"未上牌\"].count()[0]\n",
    "Ratio = N/car_data.shape[0]\n",
    "Ratio"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Brand</th>\n",
       "      <th>Name</th>\n",
       "      <th>Boarding_time</th>\n",
       "      <th>Km</th>\n",
       "      <th>Discharge</th>\n",
       "      <th>Sec_price</th>\n",
       "      <th>New_price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>奥迪</td>\n",
       "      <td>奥迪A6L 2006款 2.4 CVT 舒适型</td>\n",
       "      <td>2006年8月</td>\n",
       "      <td>9.00万公里</td>\n",
       "      <td>国3</td>\n",
       "      <td>6.90</td>\n",
       "      <td>50.89万</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>奥迪</td>\n",
       "      <td>奥迪A6L 2007款 2.4 CVT 舒适型</td>\n",
       "      <td>2007年1月</td>\n",
       "      <td>8.00万公里</td>\n",
       "      <td>国4</td>\n",
       "      <td>8.88</td>\n",
       "      <td>50.89万</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>奥迪</td>\n",
       "      <td>奥迪A6L 2004款 2.4L 技术领先型</td>\n",
       "      <td>2005年5月</td>\n",
       "      <td>15.00万公里</td>\n",
       "      <td>国2</td>\n",
       "      <td>3.82</td>\n",
       "      <td>54.24万</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>奥迪</td>\n",
       "      <td>奥迪A8L 2013款 45 TFSI quattro舒适型</td>\n",
       "      <td>2013年10月</td>\n",
       "      <td>4.80万公里</td>\n",
       "      <td>欧4</td>\n",
       "      <td>44.80</td>\n",
       "      <td>101.06万</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>奥迪</td>\n",
       "      <td>奥迪A6L 2014款 30 FSI 豪华型</td>\n",
       "      <td>2014年9月</td>\n",
       "      <td>0.81万公里</td>\n",
       "      <td>国4,国5</td>\n",
       "      <td>33.19</td>\n",
       "      <td>54.99万</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>奥迪</td>\n",
       "      <td>奥迪A6 2013款 40 hybrid</td>\n",
       "      <td>2014年8月</td>\n",
       "      <td>1.60万公里</td>\n",
       "      <td>国4,京5</td>\n",
       "      <td>36.99</td>\n",
       "      <td>69.25万</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>奥迪</td>\n",
       "      <td>奥迪S6 2013款 4.0TFSI</td>\n",
       "      <td>2014年9月</td>\n",
       "      <td>1.60万公里</td>\n",
       "      <td>欧5</td>\n",
       "      <td>59.99</td>\n",
       "      <td>114.84万</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>奥迪</td>\n",
       "      <td>奥迪A7 2011款 2.8 自动 进取型</td>\n",
       "      <td>2013年7月</td>\n",
       "      <td>3.00万公里</td>\n",
       "      <td>欧4</td>\n",
       "      <td>37.99</td>\n",
       "      <td>79.02万</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>奥迪</td>\n",
       "      <td>奥迪RS 5 2012款 4.2L 双离合 quattro Coupe</td>\n",
       "      <td>2013年5月</td>\n",
       "      <td>7.00万公里</td>\n",
       "      <td>国4,国5</td>\n",
       "      <td>56.80</td>\n",
       "      <td>130.04万</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>奥迪</td>\n",
       "      <td>奥迪R8 2016款 V10 Coupe Performance</td>\n",
       "      <td>2017年8月</td>\n",
       "      <td>0.24万公里</td>\n",
       "      <td>国5</td>\n",
       "      <td>229.90</td>\n",
       "      <td>275.49万</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>奥迪</td>\n",
       "      <td>奥迪Q7 2016款 45 TFSI quattro S line 运动型</td>\n",
       "      <td>2017年1月</td>\n",
       "      <td>0.03万公里</td>\n",
       "      <td>国5</td>\n",
       "      <td>74.98</td>\n",
       "      <td>98.76万</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>奥迪</td>\n",
       "      <td>奥迪A5 2014款 Sportback 45 TFSI</td>\n",
       "      <td>2015年12月</td>\n",
       "      <td>0.37万公里</td>\n",
       "      <td>欧4,OBD</td>\n",
       "      <td>32.98</td>\n",
       "      <td>54.25万</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>奥迪</td>\n",
       "      <td>奥迪Q3 2013款 35TFSI quattro 技术型</td>\n",
       "      <td>2014年9月</td>\n",
       "      <td>3.87万公里</td>\n",
       "      <td>国4</td>\n",
       "      <td>19.98</td>\n",
       "      <td>36.23万</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>奥迪</td>\n",
       "      <td>奥迪A4L 2013款 35 TFSI 自动 舒适型</td>\n",
       "      <td>2014年7月</td>\n",
       "      <td>4.70万公里</td>\n",
       "      <td>国4</td>\n",
       "      <td>21.58</td>\n",
       "      <td>35.81万</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>奥迪</td>\n",
       "      <td>奥迪Q5 2013款 40TFSI 舒适型</td>\n",
       "      <td>2014年9月</td>\n",
       "      <td>5.50万公里</td>\n",
       "      <td>国4</td>\n",
       "      <td>30.58</td>\n",
       "      <td>52.31万</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>奥迪</td>\n",
       "      <td>奥迪A6L 2005款 4.2FSI quattro至尊旗舰型</td>\n",
       "      <td>2006年4月</td>\n",
       "      <td>16.20万公里</td>\n",
       "      <td>国2</td>\n",
       "      <td>10.58</td>\n",
       "      <td>92.95万</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>奥迪</td>\n",
       "      <td>奥迪A4L 2013款 35 TFSI 自动 舒适型</td>\n",
       "      <td>2014年1月</td>\n",
       "      <td>9.00万公里</td>\n",
       "      <td>国4</td>\n",
       "      <td>21.38</td>\n",
       "      <td>35.81万</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>奥迪</td>\n",
       "      <td>奥迪Q3 2013款 35TFSI 舒适型</td>\n",
       "      <td>2014年3月</td>\n",
       "      <td>7.49万公里</td>\n",
       "      <td>国4</td>\n",
       "      <td>20.58</td>\n",
       "      <td>34.30万</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>奥迪</td>\n",
       "      <td>奥迪A6L 2008款 2.4舒适型</td>\n",
       "      <td>2008年11月</td>\n",
       "      <td>11.40万公里</td>\n",
       "      <td>国4</td>\n",
       "      <td>13.00</td>\n",
       "      <td>50.89万</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>奥迪</td>\n",
       "      <td>奥迪A6L 2007款 3.2FSI quattro领先尊享型</td>\n",
       "      <td>2008年1月</td>\n",
       "      <td>8.80万公里</td>\n",
       "      <td>国4</td>\n",
       "      <td>9.88</td>\n",
       "      <td>78.29万</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>奥迪</td>\n",
       "      <td>奥迪R8 2011款 Coupe V10 5.2L FSI quattro</td>\n",
       "      <td>2011年10月</td>\n",
       "      <td>3.00万公里</td>\n",
       "      <td>欧5</td>\n",
       "      <td>97.80</td>\n",
       "      <td>252.70万</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>奥迪</td>\n",
       "      <td>奥迪R8 2010款 5.2 FSI quattro</td>\n",
       "      <td>2011年5月</td>\n",
       "      <td>3.00万公里</td>\n",
       "      <td>欧5</td>\n",
       "      <td>98.80</td>\n",
       "      <td>252.70万</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>奥迪</td>\n",
       "      <td>奥迪A6L 2014款 30 FSI 豪华型</td>\n",
       "      <td>2014年11月</td>\n",
       "      <td>3.52万公里</td>\n",
       "      <td>国4,国5</td>\n",
       "      <td>31.00</td>\n",
       "      <td>54.99万</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>奥迪</td>\n",
       "      <td>奥迪A6L 2009款 2.4 舒适型</td>\n",
       "      <td>2009年8月</td>\n",
       "      <td>10.00万公里</td>\n",
       "      <td>国4</td>\n",
       "      <td>15.48</td>\n",
       "      <td>50.89万</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>奥迪</td>\n",
       "      <td>奥迪A1 2014款 Sportback 30TFSI 时尚型</td>\n",
       "      <td>2015年10月</td>\n",
       "      <td>3.00万公里</td>\n",
       "      <td>国4,国5</td>\n",
       "      <td>14.48</td>\n",
       "      <td>22.77万</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>奥迪</td>\n",
       "      <td>奥迪A5 2010款 Sportback 2.0T 技术型</td>\n",
       "      <td>2011年2月</td>\n",
       "      <td>10.00万公里</td>\n",
       "      <td>欧4</td>\n",
       "      <td>20.30</td>\n",
       "      <td>57.31万</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>奥迪</td>\n",
       "      <td>奥迪Q5 2012款 2.0 TFSI 技术型</td>\n",
       "      <td>2012年1月</td>\n",
       "      <td>13.54万公里</td>\n",
       "      <td>国4</td>\n",
       "      <td>21.80</td>\n",
       "      <td>46.41万</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>奥迪</td>\n",
       "      <td>奥迪A4L 2010款 2.0TFSI 舒适型</td>\n",
       "      <td>2011年1月</td>\n",
       "      <td>6.30万公里</td>\n",
       "      <td>国4</td>\n",
       "      <td>12.98</td>\n",
       "      <td>35.81万</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>奥迪</td>\n",
       "      <td>奥迪A6L 2012款 30 FSI 技术型</td>\n",
       "      <td>2013年1月</td>\n",
       "      <td>5.60万公里</td>\n",
       "      <td>国5</td>\n",
       "      <td>27.38</td>\n",
       "      <td>46.98万</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>奥迪</td>\n",
       "      <td>奥迪A4L 2016款 35 TFSI 自动 运动型</td>\n",
       "      <td>2016年6月</td>\n",
       "      <td>0.80万公里</td>\n",
       "      <td>国4,国5</td>\n",
       "      <td>25.68</td>\n",
       "      <td>37.88万</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11251</th>\n",
       "      <td>众泰</td>\n",
       "      <td>众泰T600 2016款 1.5T 手动 豪华型</td>\n",
       "      <td>2016年5月</td>\n",
       "      <td>3.96万公里</td>\n",
       "      <td>国4</td>\n",
       "      <td>6.80</td>\n",
       "      <td>9.42万</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11252</th>\n",
       "      <td>众泰</td>\n",
       "      <td>众泰Z700 2016款 1.8T 手动 典雅型</td>\n",
       "      <td>2017年8月</td>\n",
       "      <td>0.08万公里</td>\n",
       "      <td>国4,国5</td>\n",
       "      <td>8.80</td>\n",
       "      <td>11.92万</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11253</th>\n",
       "      <td>众泰</td>\n",
       "      <td>大迈X5 2015款 1.5T 手动 豪华型</td>\n",
       "      <td>2016年9月</td>\n",
       "      <td>0.80万公里</td>\n",
       "      <td>国4</td>\n",
       "      <td>5.80</td>\n",
       "      <td>8.56万</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11254</th>\n",
       "      <td>众泰</td>\n",
       "      <td>众泰T600 2017款 1.5T 手动 精英贺岁版</td>\n",
       "      <td>2017年3月</td>\n",
       "      <td>0.30万公里</td>\n",
       "      <td>国5</td>\n",
       "      <td>6.20</td>\n",
       "      <td>8.66万</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11255</th>\n",
       "      <td>众泰</td>\n",
       "      <td>众泰T600 2016款 1.5T 手动 旗舰型</td>\n",
       "      <td>2016年2月</td>\n",
       "      <td>1.70万公里</td>\n",
       "      <td>国4</td>\n",
       "      <td>7.00</td>\n",
       "      <td>11.59万</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11256</th>\n",
       "      <td>众泰</td>\n",
       "      <td>众泰T600 2015款 1.5T 手动 尊贵型</td>\n",
       "      <td>2015年12月</td>\n",
       "      <td>0.75万公里</td>\n",
       "      <td>国4</td>\n",
       "      <td>8.00</td>\n",
       "      <td>10.72万</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11257</th>\n",
       "      <td>众泰</td>\n",
       "      <td>众泰T600 2016款 运动版 1.5T 手动 尊享型</td>\n",
       "      <td>2016年11月</td>\n",
       "      <td>0.43万公里</td>\n",
       "      <td>国5</td>\n",
       "      <td>8.50</td>\n",
       "      <td>11.48万</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11258</th>\n",
       "      <td>众泰</td>\n",
       "      <td>众泰Z700 2016款 1.8T 手动 典雅型</td>\n",
       "      <td>2017年8月</td>\n",
       "      <td>0.08万公里</td>\n",
       "      <td>国4,国5</td>\n",
       "      <td>9.30</td>\n",
       "      <td>11.92万</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11259</th>\n",
       "      <td>众泰</td>\n",
       "      <td>大迈X5 2015款 1.5T 手动 豪华型</td>\n",
       "      <td>2016年9月</td>\n",
       "      <td>0.80万公里</td>\n",
       "      <td>国4</td>\n",
       "      <td>6.00</td>\n",
       "      <td>8.56万</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11260</th>\n",
       "      <td>众泰</td>\n",
       "      <td>众泰SR7 2016款 1.5T CVT 魔方之心 国五</td>\n",
       "      <td>2015年12月</td>\n",
       "      <td>2.36万公里</td>\n",
       "      <td>国5</td>\n",
       "      <td>7.58</td>\n",
       "      <td>11.72万</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11261</th>\n",
       "      <td>众泰</td>\n",
       "      <td>众泰SR9 2017款 2.0T 双离合 极致之梦版</td>\n",
       "      <td>2016年12月</td>\n",
       "      <td>1.25万公里</td>\n",
       "      <td>国5</td>\n",
       "      <td>11.88</td>\n",
       "      <td>17.56万</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11262</th>\n",
       "      <td>众泰</td>\n",
       "      <td>众泰T600 2016款 1.5T 手动 豪华型</td>\n",
       "      <td>2016年5月</td>\n",
       "      <td>3.96万公里</td>\n",
       "      <td>国4</td>\n",
       "      <td>6.80</td>\n",
       "      <td>9.42万</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11263</th>\n",
       "      <td>中华</td>\n",
       "      <td>中华V3 2015款 1.5T 手动 都市型</td>\n",
       "      <td>2016年1月</td>\n",
       "      <td>1.57万公里</td>\n",
       "      <td>国4,国5</td>\n",
       "      <td>4.98</td>\n",
       "      <td>8.87万</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11264</th>\n",
       "      <td>中华</td>\n",
       "      <td>骏捷FRV 2010款 1.5MT 豪华型</td>\n",
       "      <td>2012年7月</td>\n",
       "      <td>6.00万公里</td>\n",
       "      <td>国4</td>\n",
       "      <td>2.58</td>\n",
       "      <td>7.96万</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11265</th>\n",
       "      <td>中华</td>\n",
       "      <td>中华V5 2012款 1.5T 手动 豪华型</td>\n",
       "      <td>2012年8月</td>\n",
       "      <td>3.60万公里</td>\n",
       "      <td>国4</td>\n",
       "      <td>6.30</td>\n",
       "      <td>13.66万</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11266</th>\n",
       "      <td>中华</td>\n",
       "      <td>中华V3 2017款 1.5L 手动 智能型</td>\n",
       "      <td>2016年10月</td>\n",
       "      <td>0.80万公里</td>\n",
       "      <td>国5</td>\n",
       "      <td>5.80</td>\n",
       "      <td>8.43万</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11267</th>\n",
       "      <td>中华</td>\n",
       "      <td>骏捷FSV 2010款 1.5MT精英型</td>\n",
       "      <td>2011年12月</td>\n",
       "      <td>7.80万公里</td>\n",
       "      <td>国4</td>\n",
       "      <td>2.50</td>\n",
       "      <td>8.99万</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11268</th>\n",
       "      <td>中华</td>\n",
       "      <td>骏捷FSV 2010款 1.5MT舒适型</td>\n",
       "      <td>2012年6月</td>\n",
       "      <td>7.30万公里</td>\n",
       "      <td>国4</td>\n",
       "      <td>3.20</td>\n",
       "      <td>7.90万</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11269</th>\n",
       "      <td>中华</td>\n",
       "      <td>中华H230 2012款 1.5L 手动 精英型</td>\n",
       "      <td>2013年5月</td>\n",
       "      <td>6.40万公里</td>\n",
       "      <td>国4,国5</td>\n",
       "      <td>2.70</td>\n",
       "      <td>6.49万</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11270</th>\n",
       "      <td>中华</td>\n",
       "      <td>骏捷FSV 2010款 1.5AT豪华型</td>\n",
       "      <td>2011年9月</td>\n",
       "      <td>13.40万公里</td>\n",
       "      <td>国4</td>\n",
       "      <td>3.00</td>\n",
       "      <td>10.07万</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11271</th>\n",
       "      <td>中华</td>\n",
       "      <td>中华H530 2011款 1.6L 手动 豪华型</td>\n",
       "      <td>2012年10月</td>\n",
       "      <td>10.00万公里</td>\n",
       "      <td>国4</td>\n",
       "      <td>3.00</td>\n",
       "      <td>10.18万</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11272</th>\n",
       "      <td>中华</td>\n",
       "      <td>骏捷FSV 2010款 1.5AT豪华型</td>\n",
       "      <td>2011年9月</td>\n",
       "      <td>13.40万公里</td>\n",
       "      <td>国4</td>\n",
       "      <td>2.62</td>\n",
       "      <td>10.07万</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11273</th>\n",
       "      <td>中华</td>\n",
       "      <td>骏捷 2008款 1.6MT豪华型</td>\n",
       "      <td>2009年4月</td>\n",
       "      <td>12.00万公里</td>\n",
       "      <td>国3</td>\n",
       "      <td>1.80</td>\n",
       "      <td>10.40万</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11274</th>\n",
       "      <td>中华</td>\n",
       "      <td>骏捷FRV 2010款 1.3MT 舒适型</td>\n",
       "      <td>2010年8月</td>\n",
       "      <td>5.00万公里</td>\n",
       "      <td>国4</td>\n",
       "      <td>2.20</td>\n",
       "      <td>6.22万</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11275</th>\n",
       "      <td>中华</td>\n",
       "      <td>骏捷 2007款 1.8 MT 豪华型</td>\n",
       "      <td>2008年3月</td>\n",
       "      <td>7.30万公里</td>\n",
       "      <td>国3</td>\n",
       "      <td>2.00</td>\n",
       "      <td>11.48万</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11276</th>\n",
       "      <td>中华</td>\n",
       "      <td>中华V3 2016款 1.5L 自动 智能型</td>\n",
       "      <td>2016年6月</td>\n",
       "      <td>1.90万公里</td>\n",
       "      <td>国4,国5</td>\n",
       "      <td>7.00</td>\n",
       "      <td>9.63万</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11277</th>\n",
       "      <td>中华</td>\n",
       "      <td>骏捷FRV 2010款 1.3MT 舒适型</td>\n",
       "      <td>2011年6月</td>\n",
       "      <td>5.00万公里</td>\n",
       "      <td>国4</td>\n",
       "      <td>2.20</td>\n",
       "      <td>6.22万</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11278</th>\n",
       "      <td>中华</td>\n",
       "      <td>骏捷 2007款 1.8 MT 豪华型</td>\n",
       "      <td>2007年10月</td>\n",
       "      <td>8.50万公里</td>\n",
       "      <td>国3</td>\n",
       "      <td>1.80</td>\n",
       "      <td>11.48万</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11279</th>\n",
       "      <td>中华</td>\n",
       "      <td>骏捷FSV 2010款 1.5MT精英型</td>\n",
       "      <td>2011年12月</td>\n",
       "      <td>7.80万公里</td>\n",
       "      <td>国4</td>\n",
       "      <td>2.10</td>\n",
       "      <td>8.99万</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11280</th>\n",
       "      <td>中欧房车</td>\n",
       "      <td>尊逸 2013款 3.5L 尊逸A型</td>\n",
       "      <td>2014年4月</td>\n",
       "      <td>6.80万公里</td>\n",
       "      <td>欧4</td>\n",
       "      <td>53.80</td>\n",
       "      <td>168.25万</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>11188 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      Brand                                   Name Boarding_time        Km  \\\n",
       "0        奥迪                奥迪A6L 2006款 2.4 CVT 舒适型       2006年8月   9.00万公里   \n",
       "1        奥迪                奥迪A6L 2007款 2.4 CVT 舒适型       2007年1月   8.00万公里   \n",
       "2        奥迪                 奥迪A6L 2004款 2.4L 技术领先型       2005年5月  15.00万公里   \n",
       "3        奥迪         奥迪A8L 2013款 45 TFSI quattro舒适型      2013年10月   4.80万公里   \n",
       "4        奥迪                 奥迪A6L 2014款 30 FSI 豪华型       2014年9月   0.81万公里   \n",
       "6        奥迪                   奥迪A6 2013款 40 hybrid       2014年8月   1.60万公里   \n",
       "7        奥迪                     奥迪S6 2013款 4.0TFSI       2014年9月   1.60万公里   \n",
       "8        奥迪                  奥迪A7 2011款 2.8 自动 进取型       2013年7月   3.00万公里   \n",
       "9        奥迪    奥迪RS 5 2012款 4.2L 双离合 quattro Coupe       2013年5月   7.00万公里   \n",
       "10       奥迪       奥迪R8 2016款 V10 Coupe Performance       2017年8月   0.24万公里   \n",
       "11       奥迪  奥迪Q7 2016款 45 TFSI quattro S line 运动型       2017年1月   0.03万公里   \n",
       "12       奥迪           奥迪A5 2014款 Sportback 45 TFSI      2015年12月   0.37万公里   \n",
       "13       奥迪          奥迪Q3 2013款 35TFSI quattro 技术型       2014年9月   3.87万公里   \n",
       "14       奥迪             奥迪A4L 2013款 35 TFSI 自动 舒适型       2014年7月   4.70万公里   \n",
       "15       奥迪                  奥迪Q5 2013款 40TFSI 舒适型       2014年9月   5.50万公里   \n",
       "16       奥迪        奥迪A6L 2005款 4.2FSI quattro至尊旗舰型       2006年4月  16.20万公里   \n",
       "17       奥迪             奥迪A4L 2013款 35 TFSI 自动 舒适型       2014年1月   9.00万公里   \n",
       "18       奥迪                  奥迪Q3 2013款 35TFSI 舒适型       2014年3月   7.49万公里   \n",
       "19       奥迪                     奥迪A6L 2008款 2.4舒适型      2008年11月  11.40万公里   \n",
       "20       奥迪        奥迪A6L 2007款 3.2FSI quattro领先尊享型       2008年1月   8.80万公里   \n",
       "21       奥迪  奥迪R8 2011款 Coupe V10 5.2L FSI quattro      2011年10月   3.00万公里   \n",
       "22       奥迪             奥迪R8 2010款 5.2 FSI quattro       2011年5月   3.00万公里   \n",
       "23       奥迪                 奥迪A6L 2014款 30 FSI 豪华型      2014年11月   3.52万公里   \n",
       "24       奥迪                    奥迪A6L 2009款 2.4 舒适型       2009年8月  10.00万公里   \n",
       "25       奥迪        奥迪A1 2014款 Sportback 30TFSI 时尚型      2015年10月   3.00万公里   \n",
       "26       奥迪          奥迪A5 2010款 Sportback 2.0T 技术型       2011年2月  10.00万公里   \n",
       "27       奥迪                奥迪Q5 2012款 2.0 TFSI 技术型       2012年1月  13.54万公里   \n",
       "28       奥迪                奥迪A4L 2010款 2.0TFSI 舒适型       2011年1月   6.30万公里   \n",
       "29       奥迪                 奥迪A6L 2012款 30 FSI 技术型       2013年1月   5.60万公里   \n",
       "30       奥迪             奥迪A4L 2016款 35 TFSI 自动 运动型       2016年6月   0.80万公里   \n",
       "...     ...                                    ...           ...       ...   \n",
       "11251    众泰               众泰T600 2016款 1.5T 手动 豪华型       2016年5月   3.96万公里   \n",
       "11252    众泰               众泰Z700 2016款 1.8T 手动 典雅型       2017年8月   0.08万公里   \n",
       "11253    众泰                 大迈X5 2015款 1.5T 手动 豪华型       2016年9月   0.80万公里   \n",
       "11254    众泰             众泰T600 2017款 1.5T 手动 精英贺岁版       2017年3月   0.30万公里   \n",
       "11255    众泰               众泰T600 2016款 1.5T 手动 旗舰型       2016年2月   1.70万公里   \n",
       "11256    众泰               众泰T600 2015款 1.5T 手动 尊贵型      2015年12月   0.75万公里   \n",
       "11257    众泰           众泰T600 2016款 运动版 1.5T 手动 尊享型      2016年11月   0.43万公里   \n",
       "11258    众泰               众泰Z700 2016款 1.8T 手动 典雅型       2017年8月   0.08万公里   \n",
       "11259    众泰                 大迈X5 2015款 1.5T 手动 豪华型       2016年9月   0.80万公里   \n",
       "11260    众泰           众泰SR7 2016款 1.5T CVT 魔方之心 国五      2015年12月   2.36万公里   \n",
       "11261    众泰             众泰SR9 2017款 2.0T 双离合 极致之梦版      2016年12月   1.25万公里   \n",
       "11262    众泰               众泰T600 2016款 1.5T 手动 豪华型       2016年5月   3.96万公里   \n",
       "11263    中华                 中华V3 2015款 1.5T 手动 都市型       2016年1月   1.57万公里   \n",
       "11264    中华                  骏捷FRV 2010款 1.5MT 豪华型       2012年7月   6.00万公里   \n",
       "11265    中华                 中华V5 2012款 1.5T 手动 豪华型       2012年8月   3.60万公里   \n",
       "11266    中华                 中华V3 2017款 1.5L 手动 智能型      2016年10月   0.80万公里   \n",
       "11267    中华                   骏捷FSV 2010款 1.5MT精英型      2011年12月   7.80万公里   \n",
       "11268    中华                   骏捷FSV 2010款 1.5MT舒适型       2012年6月   7.30万公里   \n",
       "11269    中华               中华H230 2012款 1.5L 手动 精英型       2013年5月   6.40万公里   \n",
       "11270    中华                   骏捷FSV 2010款 1.5AT豪华型       2011年9月  13.40万公里   \n",
       "11271    中华               中华H530 2011款 1.6L 手动 豪华型      2012年10月  10.00万公里   \n",
       "11272    中华                   骏捷FSV 2010款 1.5AT豪华型       2011年9月  13.40万公里   \n",
       "11273    中华                      骏捷 2008款 1.6MT豪华型       2009年4月  12.00万公里   \n",
       "11274    中华                  骏捷FRV 2010款 1.3MT 舒适型       2010年8月   5.00万公里   \n",
       "11275    中华                    骏捷 2007款 1.8 MT 豪华型       2008年3月   7.30万公里   \n",
       "11276    中华                 中华V3 2016款 1.5L 自动 智能型       2016年6月   1.90万公里   \n",
       "11277    中华                  骏捷FRV 2010款 1.3MT 舒适型       2011年6月   5.00万公里   \n",
       "11278    中华                    骏捷 2007款 1.8 MT 豪华型      2007年10月   8.50万公里   \n",
       "11279    中华                   骏捷FSV 2010款 1.5MT精英型      2011年12月   7.80万公里   \n",
       "11280  中欧房车                     尊逸 2013款 3.5L 尊逸A型       2014年4月   6.80万公里   \n",
       "\n",
       "      Discharge  Sec_price New_price  \n",
       "0            国3       6.90    50.89万  \n",
       "1            国4       8.88    50.89万  \n",
       "2            国2       3.82    54.24万  \n",
       "3            欧4      44.80   101.06万  \n",
       "4         国4,国5      33.19    54.99万  \n",
       "6         国4,京5      36.99    69.25万  \n",
       "7            欧5      59.99   114.84万  \n",
       "8            欧4      37.99    79.02万  \n",
       "9         国4,国5      56.80   130.04万  \n",
       "10           国5     229.90   275.49万  \n",
       "11           国5      74.98    98.76万  \n",
       "12       欧4,OBD      32.98    54.25万  \n",
       "13           国4      19.98    36.23万  \n",
       "14           国4      21.58    35.81万  \n",
       "15           国4      30.58    52.31万  \n",
       "16           国2      10.58    92.95万  \n",
       "17           国4      21.38    35.81万  \n",
       "18           国4      20.58    34.30万  \n",
       "19           国4      13.00    50.89万  \n",
       "20           国4       9.88    78.29万  \n",
       "21           欧5      97.80   252.70万  \n",
       "22           欧5      98.80   252.70万  \n",
       "23        国4,国5      31.00    54.99万  \n",
       "24           国4      15.48    50.89万  \n",
       "25        国4,国5      14.48    22.77万  \n",
       "26           欧4      20.30    57.31万  \n",
       "27           国4      21.80    46.41万  \n",
       "28           国4      12.98    35.81万  \n",
       "29           国5      27.38    46.98万  \n",
       "30        国4,国5      25.68    37.88万  \n",
       "...         ...        ...       ...  \n",
       "11251        国4       6.80     9.42万  \n",
       "11252     国4,国5       8.80    11.92万  \n",
       "11253        国4       5.80     8.56万  \n",
       "11254        国5       6.20     8.66万  \n",
       "11255        国4       7.00    11.59万  \n",
       "11256        国4       8.00    10.72万  \n",
       "11257        国5       8.50    11.48万  \n",
       "11258     国4,国5       9.30    11.92万  \n",
       "11259        国4       6.00     8.56万  \n",
       "11260        国5       7.58    11.72万  \n",
       "11261        国5      11.88    17.56万  \n",
       "11262        国4       6.80     9.42万  \n",
       "11263     国4,国5       4.98     8.87万  \n",
       "11264        国4       2.58     7.96万  \n",
       "11265        国4       6.30    13.66万  \n",
       "11266        国5       5.80     8.43万  \n",
       "11267        国4       2.50     8.99万  \n",
       "11268        国4       3.20     7.90万  \n",
       "11269     国4,国5       2.70     6.49万  \n",
       "11270        国4       3.00    10.07万  \n",
       "11271        国4       3.00    10.18万  \n",
       "11272        国4       2.62    10.07万  \n",
       "11273        国3       1.80    10.40万  \n",
       "11274        国4       2.20     6.22万  \n",
       "11275        国3       2.00    11.48万  \n",
       "11276     国4,国5       7.00     9.63万  \n",
       "11277        国4       2.20     6.22万  \n",
       "11278        国3       1.80    11.48万  \n",
       "11279        国4       2.10     8.99万  \n",
       "11280        欧4      53.80   168.25万  \n",
       "\n",
       "[11188 rows x 7 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cars  = car_data.loc[car_data.Boarding_time !=\"未上牌\",:]\n",
    "cars"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Brand</th>\n",
       "      <th>Name</th>\n",
       "      <th>Boarding_time</th>\n",
       "      <th>Km</th>\n",
       "      <th>Discharge</th>\n",
       "      <th>Sec_price</th>\n",
       "      <th>New_price</th>\n",
       "      <th>year</th>\n",
       "      <th>month</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>奥迪</td>\n",
       "      <td>奥迪A6L 2006款 2.4 CVT 舒适型</td>\n",
       "      <td>2006年8月</td>\n",
       "      <td>9.00万公里</td>\n",
       "      <td>国3</td>\n",
       "      <td>6.90</td>\n",
       "      <td>50.89万</td>\n",
       "      <td>2006</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>奥迪</td>\n",
       "      <td>奥迪A6L 2007款 2.4 CVT 舒适型</td>\n",
       "      <td>2007年1月</td>\n",
       "      <td>8.00万公里</td>\n",
       "      <td>国4</td>\n",
       "      <td>8.88</td>\n",
       "      <td>50.89万</td>\n",
       "      <td>2007</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>奥迪</td>\n",
       "      <td>奥迪A6L 2004款 2.4L 技术领先型</td>\n",
       "      <td>2005年5月</td>\n",
       "      <td>15.00万公里</td>\n",
       "      <td>国2</td>\n",
       "      <td>3.82</td>\n",
       "      <td>54.24万</td>\n",
       "      <td>2005</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>奥迪</td>\n",
       "      <td>奥迪A8L 2013款 45 TFSI quattro舒适型</td>\n",
       "      <td>2013年10月</td>\n",
       "      <td>4.80万公里</td>\n",
       "      <td>欧4</td>\n",
       "      <td>44.80</td>\n",
       "      <td>101.06万</td>\n",
       "      <td>2013</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>奥迪</td>\n",
       "      <td>奥迪A6L 2014款 30 FSI 豪华型</td>\n",
       "      <td>2014年9月</td>\n",
       "      <td>0.81万公里</td>\n",
       "      <td>国4,国5</td>\n",
       "      <td>33.19</td>\n",
       "      <td>54.99万</td>\n",
       "      <td>2014</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Brand                            Name Boarding_time        Km Discharge  \\\n",
       "0    奥迪         奥迪A6L 2006款 2.4 CVT 舒适型       2006年8月   9.00万公里        国3   \n",
       "1    奥迪         奥迪A6L 2007款 2.4 CVT 舒适型       2007年1月   8.00万公里        国4   \n",
       "2    奥迪          奥迪A6L 2004款 2.4L 技术领先型       2005年5月  15.00万公里        国2   \n",
       "3    奥迪  奥迪A8L 2013款 45 TFSI quattro舒适型      2013年10月   4.80万公里        欧4   \n",
       "4    奥迪          奥迪A6L 2014款 30 FSI 豪华型       2014年9月   0.81万公里     国4,国5   \n",
       "\n",
       "   Sec_price New_price  year  month  \n",
       "0       6.90    50.89万  2006      8  \n",
       "1       8.88    50.89万  2007      1  \n",
       "2       3.82    54.24万  2005      5  \n",
       "3      44.80   101.06万  2013     10  \n",
       "4      33.19    54.99万  2014      9  "
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "car = cars.copy()\n",
    "car.index = range(0,car.shape[0])\n",
    "car[\"year\"] = car.Boarding_time.str[:4].astype(\"int\")\n",
    "month = car.Boarding_time.str.findall(\"年(.*?)月\")\n",
    "month = pd.DataFrame([i[0] for i in month]).astype(\"int\")\n",
    "car[\"month\"] = month\n",
    "car.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Brand</th>\n",
       "      <th>Name</th>\n",
       "      <th>Boarding_time</th>\n",
       "      <th>Km</th>\n",
       "      <th>Discharge</th>\n",
       "      <th>Sec_price</th>\n",
       "      <th>New_price</th>\n",
       "      <th>year</th>\n",
       "      <th>month</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>奥迪</td>\n",
       "      <td>奥迪A6L 2006款 2.4 CVT 舒适型</td>\n",
       "      <td>2006年8月</td>\n",
       "      <td>9.00</td>\n",
       "      <td>国3</td>\n",
       "      <td>6.90</td>\n",
       "      <td>50.89</td>\n",
       "      <td>2006</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>奥迪</td>\n",
       "      <td>奥迪A6L 2007款 2.4 CVT 舒适型</td>\n",
       "      <td>2007年1月</td>\n",
       "      <td>8.00</td>\n",
       "      <td>国4</td>\n",
       "      <td>8.88</td>\n",
       "      <td>50.89</td>\n",
       "      <td>2007</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>奥迪</td>\n",
       "      <td>奥迪A6L 2004款 2.4L 技术领先型</td>\n",
       "      <td>2005年5月</td>\n",
       "      <td>15.00</td>\n",
       "      <td>国2</td>\n",
       "      <td>3.82</td>\n",
       "      <td>54.24</td>\n",
       "      <td>2005</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>奥迪</td>\n",
       "      <td>奥迪A8L 2013款 45 TFSI quattro舒适型</td>\n",
       "      <td>2013年10月</td>\n",
       "      <td>4.80</td>\n",
       "      <td>欧4</td>\n",
       "      <td>44.80</td>\n",
       "      <td>101.06</td>\n",
       "      <td>2013</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>奥迪</td>\n",
       "      <td>奥迪A6L 2014款 30 FSI 豪华型</td>\n",
       "      <td>2014年9月</td>\n",
       "      <td>0.81</td>\n",
       "      <td>国4,国5</td>\n",
       "      <td>33.19</td>\n",
       "      <td>54.99</td>\n",
       "      <td>2014</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Brand                            Name Boarding_time     Km Discharge  \\\n",
       "0    奥迪         奥迪A6L 2006款 2.4 CVT 舒适型       2006年8月   9.00        国3   \n",
       "1    奥迪         奥迪A6L 2007款 2.4 CVT 舒适型       2007年1月   8.00        国4   \n",
       "2    奥迪          奥迪A6L 2004款 2.4L 技术领先型       2005年5月  15.00        国2   \n",
       "3    奥迪  奥迪A8L 2013款 45 TFSI quattro舒适型      2013年10月   4.80        欧4   \n",
       "4    奥迪          奥迪A6L 2014款 30 FSI 豪华型       2014年9月   0.81     国4,国5   \n",
       "\n",
       "   Sec_price New_price  year  month  \n",
       "0       6.90     50.89  2006      8  \n",
       "1       8.88     50.89  2007      1  \n",
       "2       3.82     54.24  2005      5  \n",
       "3      44.80    101.06  2013     10  \n",
       "4      33.19     54.99  2014      9  "
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "car[\"Km\"] = car[\"Km\"].str.extract(\"(\\d+\\.?\\d+)\",expand = True)\n",
    "car[\"New_price\"] = car[\"New_price\"].str.extract(\"(\\d+\\.?\\d+)\",expand = True)\n",
    "car.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Brand             object\n",
       "Name              object\n",
       "Boarding_time     object\n",
       "Km               float64\n",
       "Discharge         object\n",
       "Sec_price        float64\n",
       "New_price        float64\n",
       "year               int32\n",
       "month              int32\n",
       "dtype: object"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# car.dtypes\n",
    "car[\"New_price\"] = car[\"New_price\"].astype(\"float\")\n",
    "car[\"Km\"] = car[\"Km\"].astype(\"float\")\n",
    "car.dtypes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 3、车辆价格分布情况!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "min_price = car.Sec_price.min()\n",
    "max_price = car.Sec_price.max()\n",
    "\n",
    "car.Sec_price.hist(bins = np.arange(min_price,max_price+10,10))\n",
    "\n",
    "plt.title(\"二手车价格分布直方图\")\n",
    "plt.xlabel(\"价格\")\n",
    "plt.ylabel(\"频数\")\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 4、车辆销量品牌占比"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x27a2df22be0>"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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ZSETuAXapalWb+7riDZZfAjykqs8l7v8s3li+kKre5iCuySI2ZskYB+rKyjsB5+IVSGfgbf6bVHlxBvbdpZvf6S5HJfvcJnnOawp2OMynCjAlcbubqpKX+aBwWtvuM31IRPLwlsDYLiKyZ09IVd0tIntmgn6KD/aCvEVVR7tJa7KNtSwZkyaJMUhn4RVIc2lngHayPDo78OyfpwesKy5TqUZeWL8p1Em1c5LP/DofFE5ZsSyBiHwJ6A7sADqr6l17PX4XgKp+JbG8yC5gqKraXpDmiFmxZEyK1ZWVzwI+B1wAdE3ntdf245mvfjZ/ZjqvaQ5e11jsjWc3bD4mxZdZDPwM+D+qGlo+6uBMJCLX4H3IOA1vwPvfgKVAlaqGRKQbXoH4F7x9DH+P12ILcLGqRtKf2mQT64YzJgUS45A+BVyD9+LtxODt2JilDDa9pTUdm/Een7j9kKqS3wA/o6phRRque8REZAjwf3gbGp+9p+gRkY8BvwWWi8gEvJmCP1TVe0TkXOAuvO7Jq4GnROTq9tZmMuajWMuSMUlUV1Y+Fa9Amoe3D51zFV/Ie2dnV7EFKjPQz7duXzq9tXWcg0svxGtt+iNVDWEH1z8oIiLAHFX97wEeH4s3M+4yVf1R4r5HgWpVfTbx/RVAjaq+k6bYJgtZsWTMEaorK+8CXIZXJI13HGcfD50ceO4fUwLTXecwe1FteG3dxs55SZr9eJi2A78Gfk5Vg+1XZ8wBWLFkzGGqKyufAFQAnwCSPUA3aVYexaKvfSr/BNc5zIcNiEQX/3PTluNd50hQ4F94rU1/o6oh5jiPMRnFxiwZcwjqysoFbybbLXhTlTPe0HcY4DqD2depzc0H2gjaBQFOT9w2UVXyIF5r01a3sYzJDNayZMxBSEz7vxy4GShzHOeQKOhVX8zbubuT9HSdxXzgT5u2rhseiZS6ztGOEPAgcBdVDRtchzHGJSuWjGlHXVl5V+Ba4AbAt4OkHzg98MJTEwJTXecwnjzVza+t2+iXxUIjwMPA96lqeMt1GGNcCLgOYEwmqisr711XVv5dvCnL38fHhRLAjDrNpC6fnDcyHPFT0VEAXAmspKrkYapKRroOZEy62ZglY9qoKys/CrgV780h5Stsp8vRW7Wf6wzmA+c0BV3OgDtc+Xhrh11GVcnjwHeoaljmOJMxaWHdcMYAdWXlPYHb8Lrcih3HSTqF+BU35DUGO0iJ6yw5T1UXbti8s0c87vcxZAr8Ea9oes11GGNSybrhTE6rKyvvXFdW/nXgLeAmsrBQAhAITFqtq1znMNBBdWUWFErgzaC7CHiVqpK/UlUy2XUgY1LFiiWTk+rKygvrysq/CKwFvkma92xzYcZyDbrOYGBia2ib6wwpcA6whKqSf1rRZLKRjVkyOaWurDyAt9r2N4FSt2nSa+Rm7e06g4ELGpuyuTA/DTiVqpLfAV+1JQdMtrCWJZMz6srKz8XbmfxhcqxQAugQZlRxWJtc58hpqq2zW1qcbaycJgJ8Em/23PeoKuniOpAxR8qKJZP16srKJ9aVlT8L/AUY6zqPKwL5E9bYuCWXesbiy4o0O8fF7Ucx8FVgNVUl11BV4scZgMYAViyZLFZXVt6jrqz8Z8ASwDaSBWYs192uM+SyE1pacrFlry/ennOvUVVykuswxhwOG7Nksk5i/7Yr8RaTzIZZR0lTvlF7uM6Qyy5sbPL14qZHaCzwH6pKfg/cTFXDRteBjDlY1rJkskpdWfkk4AXgAaxQ2kenVkYVRLXVdY5cJKrvjg+FR7nOkQE+DqygquQOqkqKXIcx5mBYsWSyQpsut8XAFNd5MpVA0fi3dKXrHLloSCS6SrzBz8ZbHf/bwDKqSua6DmPMR7FiyfhaXVm51JWVXwWsAq7Bfqc/0ozlust1hlx0RrA55jpDBjoa+FtiUcsBrsMYcyD2xmJ8q66sfCzwPNbldkjGrlfb8sSB85uajnadIYOdA7xJVcnlroMYsz9WLBnfqSsrz6srK/8q8DJwvOs8ftO1mVF5MY24zpFLClTfPioas5aT9nUHfkNVyZ+pKsnlgfAmA1mxZHylrqy8DHgW+B5Q6DiOLwl0HLNeV7jOkUvGhkI28+vgnYc3lukTroMYs4cVS8YX6srKA3Vl5TcDr2KtSUds1jLd6TpDLjm3MWizvg5NT+BRqkqeoKrEtukxzlmxZDJeXVn5cGAR8APImdWPU2rcOu3kOkPOUI2eHmwudx3Dpy7Ca2X6mOsgJrdZsWQyVmKm2xfx9nOb4TpPNunexKhAXG12Vhp0iWtdF9Vs3jw31XoDv6eq5DGqSmwih3HCiiWTkerKyocC/wXuwVuTxSSRQJdRm7D1ltJgamurdXkmxzy8VqbzXQcxuceKJZNx6srKP4HXmjTbdZZsNnNZfIfrDLnggsam7q4zZJG+wJ+oKnmEqhL7ezVpY8WSyRh1ZeUd68rKfwk8CnRxnSfbHfuWdnCdIeupNk5raR3tOkYWuhR4haqSia6DmNxgxZLJCHVl5WOAJcDnXGfJFb12MxJVdZ0jm/WLxerybcPyVCkFnqWq5ErXQUz2s2LJOPfbTzz4CfUKpTGus+QSgW5Hb2W16xzZ7ORgc4vrDFmuCPgFVSUPUlViM2VNylixZJyprqjtUF1R+6vdJUMfrSv71BLXeXLRzGXxba4zZLPzm4KDXGfIEVfgtTINdR3EZCcrlowT1RW1I4AXgM8CbOs75YR3+kx8yW2q3DNxjRa4zpCtAqpby8KRYa5z5JAJwMtUlZzlOojJPlYsmbSrrqi9CHgJOOb9O0UCy8o/O7y5Q58NzoLloD4NDHedIVuNCEfWus6Qg7oDf6eq5JtUldj7m0ka+2UyaVNdUSvVFbXfAZ4A9l2kT6TbkklfbY0FCpvTHi5HBZTeg7frW65zZKOzg0FxnSFHCfB14B9UlfRwHcZkByuWTFpUV9R2BB4Hbm/vuHhe4cgXJ37ltfSkMgAzl8U3uc6QdVT1nKbgSNcxctzpeMsLTHIdxPifFUsm5aoragfg7e12UPs7NXfqN33liHkLU5vK7DFptdrU9iQrVl3dKxa3DWDdGwI8Q1XJVa6DGH+zYsmkVHVF7QS8ZQEOafG4zQNmzdjR65hXU5PKtNV/JzaDKMmOC4W2us5g3lcEPEBVyc+pKslzHcb4kxVLJmUSA7mfBo465CeL5C8dc9WgluIeW5IezHxIntJ/wHtqA+uT6PzGYCfXGcw+rgb+TFWJ7TVpDpkVSyYlqitqbwd+z5FsgiuBXksm3V4fC+S3Ji2Y2a/py61YShrV0EnNLbbAamaaC/yHqpKeroMYf7FiySRVdUVtUXVF7W+B7+DNSjkisfzi0S8f92VbfynFpqyK28ytJOkejy8vVtt3L4NNxVvAstR1EOMfViyZpKmuqO0B1AKXJfO8TV0GzVwz7PxFyTyn+bCB7zLYdYZsMau5pcF1BvORRgHPUVVyrOsgxh+sWDJJ0WbG2/RUnH/DoFOmvde9fGkqzm0gP86g3vVq48OS4ILGYF/XGcxB6Q8spKrkZNdBTOazYskcseqK2mHAM6RyI1yRgtePubZPa2G3d1J2jRw3bYW+7TqD34nqrgmh0CjXOcxB64q3eOUnXAcxmc2KJYdExPfr21RX1I7DK5RSP/1cAn2XTL59e1wCkZRfKwdNXRGPu87gd4Oi0RUBe131m0Lgf6kqudl1EJO57D+1W7Uiss+0ehG5XkQK23w/U0R+kN5oH626onYqsBCvOTstogUdx71y7I3Pp+t6uWTIdga6zuB3pwebo64zmMMiwA+oKplPVYlNdjD7sGIpzUTksyKyXETeAAbhFUxvJu77XOKw44EbRORSEXkK+CHwMRF5SkQec5W9reqK2lOAp/A2rkyr3SXDTnh7yFnPpPu62a4gxtBuTbrDdQ4/O78xaAt8+ttNeK1MhR95pMkpViylXxT4NvA68E28DR9fSdy3p3vpVuBJYCDwA+Ba4DFVPYU0tuIcSHVF7YVADeBs4b23S8+avKtk+HJX189WU1foGtcZ/CpfdcPgaNRa5/zvE3jjmGxhUfM+K5bST4HhQCleIXQ73nifwUBcRCYDZ+MVTvsbQ6Lpibl/1RW1n8HbENftJy+RoleP/WK3cEGXd53myDLT6uI2HuwwjQ6F17nOYJLmZKDGCiazhxVL6SfAauAB4F3gJ8DLwObEY3FgCt4UfAW+D1QDlyS65JxNS66uqL0O+BWQGfsrSd6AxZPv2BSXgI0TSZJh29y3XPrVuU1B67rJLrOBv9v2KAasWEorEQngbeo4HG9RtNuAtcAfgHygUFVfxhs0HcMrSr4KfIEPuuGcTJ2vrqi9CriXJKzKnUyRws7Hvn7Mdc+6zpEtCqMM79KsO13n8B3V2BnBYLnrGCbp5mAFkwFE1WmvTk4RkdPwxiNFE7dpQAHeYo75eMXrfUC3xPeDgPPxuuR6A28BA1W1LJ25qytqLwceIoOL6+Fr/vjc4E3/ScmCmLnm52cElvznuMAU1zn8pFM8vuyF9ZtsP7jsVQvMpaqhxXUQ40bGvvllI1X9V6J16AK8/3zVwBV43XE3quppwNN4MzJuAF4CJqvq8ao6TFVPcVAoXQz8mgz/XVlz9AXHNnQpXek6RzaYXqf2hnCIprS02ti57HYSXguT7fmXozL6DTDbiMgwEfl/wO+Atar6dVV9ArgbuE1ElgOzgJ8mvk4E/p+ILBKRV0RkqYhsE5E70pG3uqL2POB/yZQxSu0R6fjKcTd2iOR32uU6it8N36K2XcchurApWOI6g0mtDfHeHctaf/14aWVNkessJv2sGy7NRKSD6oE/uYuI6AH+UUQkD1BVTflKy9UVtWcAf8H1rLdDVBhqeGnG87dPENQ+CBwmhdhnbswLthRLV9dZfEE1+PK6jQWFPvu/Yg7em/HSp88Nf2d6nEAe8FfgonV3nm0TS3KIvaGkWXuFUuLxA1avqhpLU6F0IvBHfPjiHy4qmfTG2GsWuc7hZwJ5k1brKtc5/KJPLLbcCqXstTB2zIK54e/NShRKAOcCvy2trLH3zxxi/9jmQ6oramcAfwN82zf/Xq9xczYNmPWC6xx+NqNOm1xn8IuTmluaXWcwqfF/0TkLPx2pnLOfhy4BfllaWZNRs4NN6lixZN5XXVE7CfgHDlfmTpZVI+aNaex01FrXOfxq1Cbt5TqDX5zfGNxnf0fjb6rE7ole8PRXolfPbuewzwLfS1cm45YVSwaA6orao/EKpewYpyLS5aWJtwaiecW7XUfxo44hRhVG1FpMPkJA9Z0x4fBw1zlM8qgS+lr0sy/+KPrxWQdxeGVpZc3nUx7KOGfFkqG6orYX3l50vV1nSSYN5A9dPPn2Fep4ixg/EiiYsFZtKYaPMCwSsb30sogqjRWRG5Y/Ejt16iE87b7SyppzUxbKZAQrlnJcdUVtB+DveKuKZ51QcY8py0ZfsdB1Dj+asUwbXGfIdGc1WeNbtoirvDcv/LWN/4xPOe4Qn5oH/K60sub4VOQymcGKpRxWXVGbBzwGZPV/8u29J8ze2vf4F13n8JvRG7S76wyZ7tymYFZ+yMg1UQ1sOTP8/d1LtHz0YZ6iI/C30soa+33IUlYs5bYf402DzW4iUld2+chgx77rXUfxk86tjMqPash1jkxVFI+v7huL2QKePhfS/LfmhH/ESh089AhP1Rt4srSyJquGMxiPFUs5qrqi9ovAda5zpI1IyYsTKyOxQGHQdRS/ECg+Zp2NWzqQ8aHwFtcZzJFp0uLl00L3lWzS3gOSdMqjgb+XVtbYxrtZxoqlHFRdUXs28EPXOdItnlc4fMmkr77hOoefzFymO11nyFTnNTXZG6KPvatdXzk+VD14JyU9k3zqKcBjtmhldrF/zBxTXVE7Hm+cUubv95YCLR37TKsbdekC1zn8Yuw6zY6lJJJNNXxKsOVwx7cYxzbE+7wwPXTfmCAdOqfoEucA307RuY0DVizlkOqK2r54M99S9QLhC1v7TZu1vfdxr7jO4QclzZTlxTTiOkemKYnHl3dU9f3irbnojfjQp+eEfzg5TEGqN8S9rbSy5qIUX8OkiRVLOaK6ojYfeBwY6DqLcyJ5b47+XGlzh16bXEfJdAJ4f7YkAAAgAElEQVQdR2+wcUt7m9nSWu86gzl0tbFjF5wb/m7bfd5S7aHSypoxabqWSSErlnLHncAJrkNkDJEeSybd1hQLFLS7sbGBmcv1PdcZMs0FjU0248lnfhc9ceEVkVvnpPmynYE/l1bWdEvzdU2SSTub3JssUV1R+zHg965zZKJOTVuePf6l785wnSOTvdeZFz9/ff7kVF6jqa6J7X/cDkD4vTB9L+xL95ndP/KYrsd1Zf296yEGncd1ps95fVIZ06Pa8Nq6jZ3zcnTcn9+oEvtx9KLn74ldNNNhjCeBs9fdeXbcYQZzBKxlKctVV9SWAb92nSNTBTsPmLFq+McWuc6RyXo0MUpUU/oi37m8M8NuH8aw24dRPLCYDkM6HNQx9c/XU3xUMcPuGEbz6mbCO8KpjAnAwGiszgolf1AldFv0cy85LpQAzgC+6ziDOQJWLGWx6orazsAfyfEB3R9l01Fzpr/bc+zrrnNkKoGuIzeRlnFL8VCc8PYwxYOKD/qYeGucPS3krRtaU57x1GBz6isyc8RUabw6clPd72InZ8oOBZWllTUfdx3CHB4rlrLbg0C56xAZTyT/jbHXDGgt6r7VdZRMNXN5fHs6rtO0rInOo9uv7dse021aN2LNMTbevxHJF+Lh1PdyXNDUNCTlFzFHJK7y7sXhr2/6d3zSsa6z7OXXpZU1Y12HMIfOiqUsVV1ReyNwsescviGB3osn374zLnnWarAfx63VAzf1JFHja410Gd/lkI456oqjGHz9YCRfyO+an9J8+aobh0aiVixlsKgGNp8RvrPxRS3LxA+KnYDHbYVv/7FiKQtVV9TOAu52ncNvYvkdxrx83M2LXefIRL0aGEGKZ4OoKsEVQTqVH3j5or2PCa4KsuXhLcQjcVo3ttLh6H3HOiXTqHB4XUovYI5ISAvWzg79KLBKBx3pPm+pVI63L6fxESuWskxi4cnHgdR+xM5SjV2HzFo79NynXefINAHoMWwba1J5jZa3WygaUESgMEB4R5itj+3bK9r2GIAu47oQj8R5+3tv0/vc3uQVp3bc9TlNQft/laEatcOyqaH7um+md3/XWQ7CVaWVNR9zHcIcPFs6IMtUV9TWAGe5zuFrquFjX79vVY/6lTa2oI2/TZFFvz05L3fX6lKNP71hc0O3eLz7Rx9s0uld7frK7NCPRqZw+5JUqAfGr7vz7A2ug5iPZi1LWaS6orYCK5SOnEjha+Ov6xkq7LrDdZRMMnG1FrjO4FJH1RVWKGWedfG+z08L3T/WZ4USQDfg0dLKGluGwgesWMoS1RW1I4AfuM6RNSTQf/HkO7bEJWD7oiX0q+do1xlcmtQasuI5w7wWP/rpE8Pzj4+QX+g6y2GaAXzDdQjz0axYygKJfd8ewZtpYZIkWtBp/Gvjv/ic6xyZIqD0GbhD33adw5ULGpu6us5gPvBU7LgF54e/PUsJ+P197PbSyprZrkOY9vn9l8x4bgemuA6Rjeq7jZi9bvDpz7jOkSlmLo/n5ubDqi0nNLeMdh3DgCr6SPTkhVdGbpnjOkuSBIBHSitrergOYg7MiiWfq66onQLc4TpHNntr6DmT6rsOq3OdIxNMXqU5Ob6iVyy+rBCKXOfIdarEfhT92LN3RD+XbS0xA4GfuA5hDsyKJR+rrqjtCPwWWyYgtUSKXz32hi7hgs7vpfvSD773HpesX8fVmzYS3s/M1X/s3s1lG9Zz2Yb1zFyzmhebmwG4a/s7fGzd29y2dUtS8wx4j9KkntAn5jQ3B11nyHWqtH41euVL98YudL3PW6rMK62sOd91CLN/Viz52w+Aka5D5AIN5A1cPPmO9YrE0nXNjeEwa8IhHhtSyqxOnXgnsu9Y87O6duWRwUN4ZPAQeufnM7KoiMXNQToGAjxROpQBBQXsjiUvcp4yoN9OzbmuuAsbg35YuydrqbL7ysjNKx6LnZQp+7ylyk9KK2tsxmUGsmLJp6oras8EPu86Ry6JFHaZ8Pox16ZtwcoXmpvZHYtz+Yb1vNzcwsCCA8/c3xgO0yUQoCQvj+eDzbwdDjNv/Toa43G65iW352x6na5L6gkznKjuGBsOj3CdI1fFVXZcFK7a8p/4xEzb5y0V+gM/ch3C7MuKJR+qrqjtCvzSdY5ctLPH6Dkbj5rzfFquFYvSPT+P3w4ewjvRCC+3tBzw2AXBJmZ38paZ2RWLMqKwiEcHD+HfjY1s2U+L1JE4fmXqN6vNJEMj0VUC4jpHLopqYNPp4buCr+jIMtdZ0ujTpZU1Z7gOYT7MiiV/+i4wwHWIXLV6+MeO2d150OpUX6dzII+hBd7yMQMLCtkejR7w2P82NTG7s1csdQrkUVpYSJ4I/fML2NHO8w7HoB0MSuoJM9yZwaBtc+BAqxasPSH047zVOrDUdRYHHiitrGl/R2mTVlYs+Ux1Re0k4FrXOXKaSKeXJ3y5MJLfoSGVlxlTXMyboVYANkTCB+yGa4rFeCcaZXhR0fvPW9baSkyVrdEIA9rpvjsc+XGG9GzQfTduy1LnNQbTuhhnMKyc91gzM34V5NZ/t+73mK2NcU79bZCpvwxy7+IQAJGY8tm/tHDMT5u4o3b/z/MLb5+3+3tsoVeujhUbBPyP6xDmA1Ys+Uh1RW0e8HPs3805DeQPWTL5jlWKpKxP6tgOHegWyOPi9esYWlhIDOVXO/edkPd0MMj0jh+sR3pqly5sjka4eP06zulaQu/85E+WnLYiNxanLIzr2v6xWFrfsP93aYSpR+Xx7BWdWLYjTt2OfQfo37ckzBXHFvLClZ148NUITWHlkTcizBqcx8tXdyIcA7/u+7ldS14+PlRdWk+XXB/ofHVpZc2JrkMYj00595frgAmuQxhPqKjb5DfHXLlg3LJfzEnVNb7Rr9+Hvj+uQ8d9jjmza1fO7PrB4tKFIvxowFGpigTAtBXx2N+Pz/6afVwotBnSu81Lt2JhfX2cWFxpiSiFefsOl+pWLDSFlVDUK4gEeOrtKIV5wn1Lwlw2rgAR/w2zeive7/nTw3dP9PH2JckkwC9LK2vGrbvz7GbXYXJd9r/aZYnqitqjgG+7zmE+bEev8bO39J++2HWOdBvyTm6MmTuvKVic7mteUJbPk2ujHH1vE+W9AhzdY9+X6WsmFvK9Z0IMv6+Jy8YV0KlQ2BFUTirNY+FnOvGdp0OEY/5qWXolPnzRyeEf+Hmft1QYBnzVdQhjxZKf3AvYgL9MIyIrRn6yvKnTgJzoltqjMMbRJUF913WOlFKNnhpsLk/3Zb//TJjPTypk3Q1d2NmqPLdx3wH6N/6zlV+c04F1X+rM31dH2dAQp2uRMKpXHl2LhI4Fws4W/xRL/4pNXHBh+FsnZME+b6lwS2llzTDXIXKd/WL6QHVF7VzgQtc5zAGIdH1x4q0azStqdB0lnY5foWtcZ0ilrvH48s6qaf+A0hhSihMDJIryhKbwfo4Je8fkBQQBWqPKxP55vLQlRkOrEopCr46Z3w2niv42esrCqyM3z3GdJYMVYWsvOWfFUoZLbGlyv+scpn0aKBi2ZNJtyxT883H+CE2vi4dcZ0il6S2tO11c9wtTCvnpSxGmPRikJap0KoAfPPfhv+rKGUXc9M9WpvyiiWP75TGyZx4Vkwr568oIJzwU5FsnFpEfyOxiSZXoD6IXP/u16BXZts9bKpxray+5JX6dMZErqitq7wJudZ3DHJy+2xYvGLPiN3Nc50iHUD6rLr8lP2u32/n5tu1vTm9pHes6RzZSpfXW6NVv/D42Z4rrLD6yChi37s6z99PWaFLNWpYyWHVF7QjgRtc5zMF7p++UE7b1mfSS6xzpUBhleOcWrXedIyVUd09pac2lVaPTRpWGz0W+vNIKpUM2ErjBdYhcZcVSZrsbSO6Kgia1RALLyz8zvLlDnw2uo6SaQGDyKl3lOkcq9I/G6vJtaZWki6nsuDD8zW218QnjXWfxqa+VVtbkxEzUTGPFUoaqrqidDZzvOoc5DCLdlkz6amssUJj1a6NMX64H3rDOx05tbs7q8VguRDRv02nhu5tf1RGjXGfxsc54H6JNmlmxlIGqK2oFmO86hzl88bzCkS9O+sprrnOk2sgt2tt1hlS4oDGYU/vfpVqrFqw5IfTj/LV61BDXWbLApaWVNTNch8g1VixlpsuAia5DmCPT3LHf9BUjLlnoOkcqFYcZ1SGkWbVkQp7q5uGRyFDXObLFbu3w5vGh6l5b6dnvo482B+ku1wFyjRVLGWb+vLnFrbvuuT7S8szTqvHkbhdv0m7LgJkzd/Q65lXXOVJFIG/CmuwatzQyHHnLdYZssV27vTQ1VD20gc7dXGfJMjNKK2vOdh0il1ixlHm+ALHJsdYls0L1926Otr74rKqmbLNWk2IieUvHXDWopbjHFtdRUmXG8uxqWZrbFMxznSEbvBXv/9z00L3jmynu9NFHm8Pw3dLKmsxeTCuLWLGUQebPm1sC3PbBPfEh0ZanZ4Tq71sbDb2Rc/uPZQ0J9Foy6fb6WCC/1XWUVCjfqD1dZ0gaVT27KZi1a0ely8vxEYtODv/P1Cj5Nps3dcYDl7gOkSusWMosXwF67Ht3dES0+anjW3fdvywWXvly2lOZIxbLLx798oRbsnL9pY4hRhVGsmNWXAfVlT3j8V6uc/jZk7HJCy4Kf9P2eUuPb5VW1tgSF2lgv8wZYv68uQOAL7V/VHhMJFgzsbX+J6/FIuuWpiWYSZqmzgNnrj76gkWucySbQOGxb+lK1zmSYWJraJvrDH6lij4cPW1hReTGOa6z5JDhwBWuQ+QCK5Yyx1eBjgd1pLYeG2n647jW+gdejEe3rEhtLJNMGweePO297uVvuM6RbDOWZ8dK3uc3Bbu6zuBHqkTvil7y3Dein7F93tLv66WVNcWuQ2Q7K5YywPx5c/sCVx7yE7VpcrjxsVGhhl+9EI/usBk8fiBS8Pox1/ZtLez2jusoyTRmvfp/tpNq65zm5tGuY/iNKi23RK955Wexc23tHzeOAq5zHSLbWbGUGW4CDveTgWi8fmq48bdDQrt/80w8tmtTMoOZFJBA3yWTb98el7ys2RCzSwuj8mPq65+nRzy+vEgP+/9hTlKl4YrILaueiM22fd7cqiytrLFW0RSyYsmx+fPm9gA+n4RT5Wns3Znh3b/uE9r92CKNN2ZVy0W2iRZ0HPfKsTe+4DpHsgh0GLtOfd0lPLu5ZbfrDH4SU9lxQfhb2/4bP872eXOvJ3Ct6xDZzIol974IdEni+Qo1tuWEUMMvuoYb/7BQ4807k3huk0S7S4ae8FbpWU+7zpEsM5fpLtcZjsSFjU22wvRBimjexlPD/9P8mg63fd4yx5dKK2uKXIfIVlYsOTR/3twueMVSKnSIR9fPDjX8LD/c9LcFqiH71JyB1g05a8qukhHLXedIhnHrtLPrDIdLVN8bHwrbG/9BaNHC1bNC9xS+pQNsn7fM0g/4jOsQe4hIVnXNWrHk1ueB7im+Rtd4ZPWcUH11NBL810LVSFash5M1RIpePfb6buGCLu+6jnKkugUZFYirL7foGRyNrhSw1ZA/QoN2XDo1dH/vbfTo6zqL2a8vl1bWpO19XUR+JiIviMgCEdkmIm1/L767n+NPEZHvpytfMlmx5Mj8eXOL8QZ2p0uPWPjN2aH6+3dHmhcsUo35ejBuVpG8AYsn37EpLgFfFhp7CHQu3+jP9ZbOaGqOuc6Q6bZp95emhqqPtn3eMtpw4GNpvF4UuERV5wBPAjERuUpE8oGQiEwVkUUi8qSIPAl8D/j4nu9F5D8i8sk05j1sViy5cxXg4NOZ9o2FXjkhVH/f9mjL88+oxu1NIgNECjsf+/ox1z3rOseRmrlMfdlCdn5T0zDXGTLZmviA52aG7hnfQtHBrQVnXPpKGq8lQFcR6QYUJr4/H4gBqOoLqnqCqp6hqmcAtwKP7PleVU9W1UfTmPewWbHkwPx5cwuAW9ymiA+Mtj4/M1R/3/po6yvPq6q6zWN2dR81e/2gU3xdMI1/Szu4znCoClTfHhiNHeU6R6Z6MT5y0anhu22fN/+YUFpZc2qarvUccD1wJ7AbCAHa9v1ERPJFpF5EFgBVwJw23Xa++ZBie8q48UlgkOsQntiwaMuCYdGWZ1YWdDx5d17RmMmuE6XKv197jNfffoaORZ25+vRvk5/34df+l9bUsmjZXwDYXr+Jz536dUYMGE8o0sJv/nsXV51WlfKMa4edP6Fb/ZqVJY3rfDnYuGcjo0Q1riK++SA2JhTeCAx1nSMT/SM2ZeG1kRtsVW7/+Qrw71ReQESKgNeBIXitSo+xn3F/qhoVkVdU9aS9nv8Q4JsP6b55Qcsy17sOsK/oqEjzPye31lcvjYXXvOY6TbK9u3sLW3et48sX3M/oQVOoD+7Y55hJw0/ipvPu4abz7qGkYw+O6jmMd3dv5X/+9AU2v7c2PUFFOrxy3E0dI/mdfDkNX6Bk+BZWu85xKM5tarLp1ntRRX8VPcMKJf86ubSyZlKKr9EVuBQ4FW+s1PeAs/c+SETygICILBeR1SLyloi8AZQCkRRnTBorltJs/ry5U4CJrnMckIbGRYJ/Pba1/mevxCIbl7mOkywrN79Kc6iJH/3lBtZuW0rPLv0PeOy7u7fQoagzHYu60Ktrf+64+FdpTAoayBu0ePLtaxWJp/XCSTJzWdw/C6Kqxk4PNpe5jpFJVIl+P/rJ574V/ZQVSv725VSeXFV3qOpXgXeBSuBxIA4UJFqd9hwXSwwA/yvepKYHgFtVdY6q+mbHCSuW0u8LrgMcFG2eEGn6/ZjWhl8siUe3+aqlYH+aWurpUlzCjef9mF1NO1i7bekBj126/nnGDD4+jen2FS4qmfTG2Gt8uWDlhLVa6DrDweoc1+Vd41riOkemUKXlpsjnX30gNtf2efO/C0sra1K60KqIXAyEE7exQFhVT1fVEIn6QkTyRORKvFanf+55auKx00TkhFRmTBYrltJo/ry5vYB5rnMcknjjlHDjo8NDDb9+Lh57b53rOIeruLATfbp5w8R6dR1AffDAk7beXP8CY4dMTVe0A3qv17jZmwac4LstUXrXM8J1hoM1tbX1PdcZMoUqDZ+OfGX1n+KzsnbcYo4pAK5O1clFZALerO5r8LrUOgC1iccWAs8kuuBeAS4ETlfVMLAU+LaI/B24C/DFLhNWLKXX5wA/jo8Qje+aHt798MDQ7keeiccatrgOdKgG9RrBhh2rANixezO9ug7Y73Et4SD1wR30716axnQHtmrExWMbOw9M04Cp5AhAz9Jt6ovMFzQ29XCdIRPEVLafH/7WO4vi449xncUk1TWllTUpmcilqq8Ap6lqk6ouUdWbVbU+8fBJqvo9VY0BU1X1LFXdknhejapOUtW5qnqcqr6ZinzJZsVSmsyfNzcAVLjOcYTyNbZ9Znj3gz1DjY8v1HjTvqOkM9SwfmPoVNyVu/94LX1LBhGPx3jq9cf3Oa5u44uUDUz1uMhDINL5pQm3BCJ5HRpcRzkUM5bHM7+gVm2a1tJa7jqGaxHN23BK+H9aX9fhI11nMUk3AG/do5Q40JIziSJpz5+zYtcIseV10mP+vLnn4A1wyybBQMHQlwo6njFeAh1sVd8UKmrduWT6C1+b7JctOTb34Lkbr8mf7jpHe/pGo0ue2rglq/avOlQtWrhqTuiH3d6hRx/XWUzK1K678+yTXYfwO2tZSh9/DOw+NJ3ikbdnhxp+KuGmfyxQDTe5DpStQsU9piwbfcVC1zkOVv9dHO06w0c5OdiSFZ94D1eDdnzj+ND9fa1QynonllbWDHcdwu+sWEqD+fPmjgBOc50jhUrikRVzQvX3t0aCTy1Ujba6DpSNtveeMHtr3+NfdJ3jYASUvgPf1fWuc7Tngqamga4zuLJNu784NVQ9fDedbSZg9hNSONA7V1ixlB4V+KT75Aj1ioXfmB2qv29XpPnpp1Xjvt4YNuOISF3Z5SODHfutcx3lYExfHt/gOsOBBFS3loUjGd/6lQqr40c9NyN073G2z1tO+UxpZY1vlvTIRFYspdj8eXPzgctd50gv7R8LvTgrVH/v5mjrkmdV1ZeLK2YkkZIXJ34lGs0ryvguzymrNGNfX4aHI2+5zuDC4njZwlPDd0+LkWdbXeWW3qRwoHcuyNgXsyxyGt4vag6KD4m2PDMjVH/vW9HQ675bLyhTxfMKh784sfLAq2pmiKPeY4jrDAdydjDoOkLa/T02deG88Ndng+RCK7fZ16dcB/AzK5ZS71LXAdyLDY82/2dq6677lsfCK15ynSYbtHTsM61u1KULXOdoT16cgX136WbXOfahquc0BXNmmrwq+svoWYuui3zRti/JbaeXVtb0ch3Cr6xYSqH58+Z2As5znSNzREZHgv+Y1Fr/k9djkbffcJ3G77b2mzbrnd4TXnadoz3T6nSd6wx7K1Zd3TsWz4nWXlUi34te+vx3opf5YksJk1L5wMddh/ArK5ZS6wKgk+sQGUdbx0ea/nRMa/3PX4pHNtW5juNbInnLRl8xrLlDr4zdjPL4lfGMW8jtuFBoq+sM6aBK842Ra1//RezsjF7vyqSV9XQcJiuWUst+MdujwUnhpsfLQw0PvhCPbvfF9hgZR6T7kkm3BWOBgoxcM2jwdjJuev55jcHOrjOkWlyp/1Skcu2f4zMzaDl6kwGml1bWZOxYwkzmu2JJRIpFZFDiz0V7PZYxUyPnz5vbBzjVdQ4/0HjD1HDjI0NDDQ8/G4/tzNjp5pkqnlc06qWJt77iOsf+FMQp7bFb33Gd432qoZOaW7J6i5OYyjvnh7+94+n4MeNcZzEZR4BPug7hR74rloDhwI8Sf64VkQV7bsCzItINQET+KSLvd4GJyL/TnHMekJfma/pZQOPvzQjvfqh/aPejT2t8d050lSRLsNOAGauGfzwjV/ieuiJzNtXtHo8v76CatesLRTRv/cnhH4Tf0KNHuM5iMpYVS4fBV8WSiPTDq4yfFpEewFXAQ21uXwQKROQLQERVgyLyaRH5L3CsiDwlIn9IU1zrgjs8BRrbNivU8Mvu4cYnFmq8+T3Xgfxi01GzZ7zbc+zrrnPsbdqKzFmcdGZzi682JD4UzVq4cmbong7rtP8g11lMRhtbWllzjOsQfuOrYgkYCszFG9E/BjgR6AasA3bjLf44BrgYGCUi31XVh1X1RGCxqp6iqhelOuT8eXOHA8en+jpZrjge3TA71PCzwnDTXxdovDVr3+SSRiT/jbHXDGgp6pFRrXJD32GA6wx7XNgUzMp90Oq10xvHh6r72T5v5iBZ69Ih8lWxpKrP43XBhYFn8KZCfgaoAr6M9/PMBL4CrAHuFpFfi0gtMFhEXhORZ0Qk1StqfyLF588lXeKRNXNCDT+Jh4NPLlCNNLsOlNEk0HvJ5Nt2xiU/5DrKHgVRju4aVOcthKK6a0JrqMx1jmTbqj1enBq6f0QjnWyfN3OwLnEdwG98VSwlfBdoAHoC64E7gZ8B9wILgD8CLwEx4CTgKlU9SVWPwSuwLgceTXHGC1N8/lzUPR5ePidUf39TpPm/C1VjYdeBMlUsv8OYlyfctMR1jj0EZMpKXe06x8BodEXAn695B7QyPvDZmaF7jmulqIPrLMZXhpRW1hzrOoSf+OqFQ0Q+BYzFK4SqgBvxuuCuS3y9HG9rke8D0/G67e4Qkf+IyFN4C0Q+Qgpnqc2fN3cwYL+EKaN9YqFXZ4fq790eaXnuadV4zHWiTNTYZcistUPPfdp1jj1m1Knzlq7Tg80ZM3YqGV6Ily88PXzXdNvnzRymc1wH8BNfFUvA34A7gDeB64FeeC1L5YmvE4E4UAM8p6o/BO4B/qyqpwB/AS5V1SdTmPHcFJ7bvE8HxlpfmBWqv3dDtPXl51Q14xY/dG394NOO39lt1JuucwAcvVX7uc5wfmOw1HWGZPlLbNrCS8Jfs33ezJGwYukQ+KpYUtVdgCb+rMA6VZ0DPJ/4+iSwQlUX7PWcsSKSrp/ViqW0ig+NtiycHqq/b1U09GbGdD1lBJHC18Zf1ytUWLLddZSiCCM6taizQfr5quuHRKO+nyWmSvzn0bMXfSlyve3zZo7UpNLKGucfYvzCV8VSQkHiBjAisb5S58TXM9o8VrBn0UpVvQa4DTgG2JGqYPPnze0K2IuYE9FR0eZ/TWmtv//NWHj1q67TZAwJ9Fs8+fZtcQlEnMaAwKTVusrV9UeHwutdXTtZVIl8O3r5C9+PXmr7vJlkEOBs1yH8wnfFkqo+r6q3J76dpKpz2tz6qeqWxHGnq34wTkJVv6OqJ6hqMIXxTgMyZhXxnKThsZHg345rrf/pq7HI+ozognItWtDpmFfHf+k51zlmLE/p/712ndMU9PX/S1WCX4xc9/qvYmfaPm8mmawr7iD5rlhqS1V3u86wl7NcBzAJ2nJcpOkPY1vrH1gSj25d6TqOaw3dhs9eN/j0Z1xmGLlZezu5sGr8zGCzb5cMiCu7Lovc9vbf4tNtnzeTbKeWVtYUuw7hB74uljLJ/HlzBa8b0GQSbZoSbvzdyFDDr5+Lx95923Ucl94aes6k+q7D6lxdv0OYUcVhbUr3dTup1pXE493Sfd1kiKlsOyf83feejY8d6zqLyUod8ZbYMR/BiqXkORbo7zqE2S/R+K7p4d2/GRza/dtn4rH6Ta4DOSFS/OqxN3QJF3R2skCkQP6ENekftzSlpTVl4xRTKax5608Kz48u06HDXWcxWc264g6CFUvJc6brAOYj5Wlsx8zw7l/1CTX+30KNNzmfJZZuGsgbuHjyHesVcbI+1Yzl6e86v7Ap6LtWpWYtWjk9dF/H9dpvoOssJuvZIO+DYMVS8lgXnH8UanTz7FDDA53DjX9aoPGWXa4DpVOksMuE14+51sn4pfKN2iOtF1QNTm9uGZ3Wax6hXdr59eND9/d7l25uxniZXDOotLLmaNchMp0VS4NF0sEAACAASURBVEkwf97cYmzjXD/qGI++PSfU8NNAuOnvC1RDja4DpcvOHqNnbxh4YtpnyHVqZVRBVFvTdb3esVhdoY9mqG7Wnkumhu4fZfu8mTSzJW8+ghVLyTEVH70gm32UxCOr5oTqq8OR4L8XqkbT9mbu0pqjLxq/u8vgtO7ZJlA0/i1N2+zEk5pbnC1XcKhWxAc9c0LoxxNCFNrsJJNuc1wHyHRWLCXHLNcBTFL0jIWXzg7V31cfaV70tGrM6UKOKSfS6eXjbi6M5HdI68raM5Zr2ro9L2gMDkjXtY7Es7ExC88I3znD9nkzjljL0kewYik5rFjKKtovFnppVqj+vm3RlsXPqGrcdaJU0UD+kCWT71ilSNp+xrHrNS1dTAHVd8aEwyPSca0j8afYjIWXRm7PiX3eNBZl+xPffP/7d2t+yNbf3Mz2P3yLA+2JrdEw25/4Jlt+dR3v/n0+qkrrhjfY9sitbHvkVjb95DM0Lf1Pun6EbDW4tLJmqOsQmcyKpSM0f97cPGCa6xwmFeKDoq3PzgzV3/t2tPXV57N1s95QUbfJS8dcuShd1+vaTFleTFPeajcsElmT6mscCVXiP42es+jGyBdy4lN9PBJi68M30LLuNQBaNy1D4zH6f2o+8VAzrW+/st/nNS37L3ldejLgivuJtzbRuu5VigcfQ7/L7qbfZXdT2LuUwr7D0vmjZKuc+D08XFYsHbkJQGfXIUwqxY6Otvx3Wqj+/hWxUN1LrtOkwru9xs/e0n/64nRcS6DDmPW6ItXXObOpOWOLW1XCVdFPLb4r+omc2ectUFDEgCvuJ79LLwDyOnaj68TEvuPtfA5pXf8GxaXHAVA8+BhaN7zx/mPxSCuR+q0U9rFGkSSY4zpAJrNi6chZF1zOiJRHmv/fpNb66jdi4bded50mqURkxchPljd1GpCWVc5nLdOdqb7GuU3BjFzMUZXg9ZHrlz4cOyOnW6QLehxF0YBRNK96DkQoHjphv8fFW3YTKOoEgBR1JN7ywSLwreteo3jI+LTkzQHWstQOK5aOnBVLuUZDx0SCfx7fWv+zl+ORTctdx0kaka4vTrxVo3lFKV9CYdw67ZTK8xfF42v6xWL9UnmNwxFXdl0auW3d3+PTJrrOkgmaVy9m98t/o89FX0cCefs9JtCxK/GQN6lRQ0ECHbu+/1jLmiV0PHpyWrLmgNLSypohrkNkKiuWjkBiP7iZrnMYR7R5Yrjp8dGhhl8ujkffyejxMQdLAwXDlky6bZlCSruwujcxKhDXlK0iPj4U3pyqcx+umAa2zg1/b+dz8bFjXGfJBLGm/8/enYfHVZV/AP++d7bsa9Ome7bShS5pm7Z03wB/OmyiiBsqiiCCoCJSUSEgy4AgyKIiiBWVTREFqyK2NEnXJG3TNd3STtq0zb7OPnPv+f0x05K2aTMzmZk7y/t5nj40M3d5B2jzzTnvPacLvdXvYPhnHoRkSLngccnjZ8BxdAcA35TcuOkA4Gv03s0jS6HFP/xfAIeloZkMYJjaRTB1CaV3nqvvz0XOntUbFbmzUe16hsqRPOyyfZO/WhHOexCQPrEJYVtv6VqL9cLffVXgElrzctfT8j5RwCsl+1j2rIVs6ULr2w+g+U8/hGXXf+HubkbXut+ddVzqlOWQLR04+eqdkJLTz4Qj16mD0A0bC9LyEnchVKZ2AdGK4vQBn4h4+sarbgXwktp1sKjiIc2IzfrUq0tIkxG7GysLoUypX709v7U2bH95flhKFS9/UhP6Pgkh3Fsam5ypQkTFgxc2Ydi/xPlMLm9fwmLABrPJyKNLA+CRpaHhvgN2Lq2QWxY7e1/JcfX9pUIo1na1CwoKkbRv8tdKrMkjwjZSVnpEJIfjupmKsi9aglKXSKub63xxFAclFiNKC1atifv1voLBYWloeLKcXYhB8Rxf6ux5Kcll+cd6oTgiukp2SBBl1ZStcsqS3haOyw/rxSUIw9D2QrsjKjZGbhLDtl7mfGGSBf06khmLbmkALlG7iGjEYSlIT994lQRgqtp1sKiXprgbljl7fiVc1n+vF8IVM3uVAYCi0V9SU3ZfXTiuTUBW8SmEfG+66/ssqo/i7FPGbVjifLaM93ljMYhnTAbAYSl4xQDC+vgziytZiqt+mbP7BZvbtq5CCI9T7YL8ZUvJX7D/ki+EpeF70V6lOaQXFKKnzOGcFNJrBmiDPLXiU67HFyq4wLPwjEW3gRe8SnC8aWPweAouRD7a34DdTc1I1utw88IyaDXnZ/j36vahoa0DIzMz8Pm5M1B54Ah2nfB+nz3V3Yd7PrEYOalR9QDUheTJzrqlsnPnSU3SnAZt0oL5RFLU/zk8OXLhopzOfTuGt++cGcrrzj4sdH+4InTXG+2R92uAeaG7YmDekRdX3OO+nRf3Y7GMw9IAeGQpeByWQqDDYkNzTx/uunwhJuXnocfuOO+Yw63t0Gs1+N4Vi5Gdkgy7y40lE4tw54oFuH3ZZUhP0iMrJSy9wmEkRsmO6sXO7udOeBw1G6N+s14izZ5LbxlnT8oN6fpFw3sQ0lW2r7DaVBmxEwLKC55rqzgosTgwi5u8z8dhKXgclkLgUEs77G43Xly3GUfbO5GTen7oOdTSjrY+K375v41wuN1I1uvOvHekrRPjc7MhxeyG7cp4j71qobP7+QaPc3dE9mYLGkm51WX398qS9vxEGyRJIG9cqzgSqut92mKJ+ArEQsD1oOerW5/y3MiPXLN4kAmAdyY+B4el4HFYCgGr04U0gx53rJiPHrsDR9vPf5DJ4nQhPyMd31mxALtPNKPLaj/z3r6TrZg8angkSw4TzwSP7cN5jq4X9squA9vUruZCZG3S5G2z7g3pZsKL9ipNobiORoimIrcnomFJCFjucN+95zX5Ewm9zxuLOzwVdw4OS0F4+sarsgGMU7uOeGDQaZGX7l0SJyc1ZcBpuCStFnkZqZAkQlZyMnodHx9zoKUNE0eo/vBTCLkudVvXzHZ0/6pOdpt3q13NQCxpYxYdKr6+MlTXKzskQtKzNcnlisgmwKcpgjq/4P5x47+UefyNhcUbVR+SiEYcloIzXe0C4sWY7Ewc7/QuQdRusSF3gCbtMTmZaOrsgaIIdNnsyPb1J7X09iE9yXDWtFzcEI5St+Vv0xzdv61RPCf3q13OuY6PWTG/I2fKrlBca2QnCkNxnass1og1ynuEdMroeqxri3Ip7/PG4tEEtQuINhyWgsNTcCFSMCwbqQYdnv1wA4anp0IRCtbvbzjrmGmjR6LTascv/7cBs8ePRkayd+mafSdbMWVkPEzBXYSwzHH1vTnR2fPqZsXTFrLeniEj0u2cdnu+w5A15Ef/NQIjR3WIY0O6iBCK0WKbONRa/OEU2qPLXb9Q6sV43ueNxStemPIcvDdcEJ6+8aoXAXxb7TpYwpFJM2yzLvXqAkmTPUbtYgBA67HtXrRx1URJyEPazfTtRdKGvy6WFgV7foqi1G9tbJo8lBr8YRVJ9Uucz+R1IJM30GbxrMtsMuaoXUQ04ZGl4BSoXQBLSBohty9y9f5+uLP3zUqh9LWoXZBHmzJte+n3tgz1OnMPKkN6nLHM4Qz7v4tOkV431/niGA5KLAFkF6xak6t2EdGEw1JwQtJjwViQ9EI+ucTZ83KGq++dCqHYOtUspjezcMmRAmPVUK4xpn1oD0x8us+SOZTzB3NcGbb1MucLk61ITg/nfRiLIjwV1w+HpeAUqF0AYwCSFU/jUmfPb7Quy/vrhXD2qlWIefwn53ZlTdgb7PlaBWPzusXJoE4Wwr7EZp8S7L0Hs1cZv2Gp69kyF3SGcN2DsSjEYakfDksBevrGq/IBxNpy0Sy+ZSjuQ8uc3S963Nb/Vgjhtg9+SogRGXbM+E6OU5feFuwl5u8XQT36P0xW9umBsASZSnlahdH1+CLe540lIH4irh8OS4HjKTgWrXJk156lzu4Xet229ZVCyK6I3p00I6vn/uSEQpInmNMv268EteXLUpvdEsx5g3nbs7TiK+4f8fYlLFHxyFI/HJYCx2GJRTkxQnZuX+Lsfr7VY9+8QQhFjtSd3bq00rrpd24M5tzxrQjqCb/rLZb8YM67ECEgP++5ruqHnts4KLFExiNL/XBYClyB2gUw5h9ljMexeZGz+/lGj2P7ZhGhdUK6sycubRx7ecCBSSejMMsiAprGIyHapjldIfsJWAg4f+q5ueZpz+d4nzeW6MaqXUA04bAUOB5ZYjFGLvLY1893dj9/UHburYnEHRuKrpvVk1F4INDzLtsvDgdyfIHbc4iAkOyiLAQst7vv3vsn+YrLQnE9xmJcbsGqNUNaPy2ecFgKHIclFqM8E922D+Y4ul/cLbsO14X1VkTJ20u/l+LWpp6/M/JFzK9X3IEc/0mrNag+p3Mpgjo+7/rJsf/wPm+M9RfSKe5YxmEpcByWWGwTzmlu63ulju7fbJfdx4N+3H/Q20iasVvn/PiIAPkdaIqaMTKQe1xrsQ55yxGPkE5+yvV4z1YxJWzLDzAWowL68xjPOCwFLiq2mWBsyIRtltvyl0sdPa9sVTzNh8JxC5chc/auabdV+nu83oOSdJvwa5FNvRBHRnnkIf1l7hTaI8tcz2C/GFc0lOswFqc4LPlwWArA0zdelQmA53BZfFF657n6Xi929qzepMgd5lBfviN32rKmUUv82hKFAJp7wL++pWkO5/Gh1GURSfvmO5/PbBJ5o4ZyHcbiGIclHw5LgYnzLe5ZApOE0rnA1fuHMc7eP21Q5J7gVtO+gIMTPje1L21Mgz/HLqgXfi2qea3FGvTisB0ifcc854tjO5HJ+18xdmEclnw4LAUmT+0CGAszrZBbF7l6f5fr7Hu7QiiWoFfkPgtRWu2sezVuTXLPYIeWnBQjBr2eEJ4rrLbJwZRyTMnbMt/5whTe542xQXFY8uGwFBgOSyxRGISnaamz57cpLsvf1wvF3j3UCwpJW1A95/4DArjoek9JbkxIdoiL7nOXroh9aUIEHHZ2KwVVy1zPzOF93hjzC4clHw5LAUjRpGfpSN+ndh2MRVCq4j6yzNnza3JZ/rVeCNeQthZxJuXM3Tvl6xUXO4YATdkhcfBixyyw2/1qAu/vI3nG+qtdjy3mfd4Y8xuHJR+t2gXEkqvHfXsEgHQhhAdAtwK5T1Y8Vo9w2V2K0+WUbbJTtgm7bCG7bNHaPX0Gh2xNtsvWNKdszXApjgxwQGWxKVNx71/m7N7frjHM2KZNXjqPSJsUzIVa82YtPZW/r3pk85a5FzpmYb2wVE278DU+bbEG1Gv0pmfZ+lWeW5cFcg5jDNzT58NhKTBZAEBEWgDDNNAO02i00CMJKX6c7NtuokdA6ZGFx+JR3Ha34nQ6FbvHF7Lg8Fi0dtmit8uWJIdsTXXI1gynbM8UUHRh/WSM+WeY7Ny5VHbuOqUxlB3WJi+cTyQF9vcIEdVP/PLEjF6zOdXWXDDQIRObxLALni9E7zy7w69+JSEg/1K+ftOzns8uC6hGxhgAcF+fD4elwGQN5WQiIgCZBE2mRBroJAOSkebXuUIIi4DoUYTH4hFum1txOV2y3e1UbIpdtsLhsUhnQpbHmuKQrelOxZYpC0/QTwwxdmFipOysGSk7tzVqkxc0aQxz5hOR/6OmRJk1s1e1L9p0n0UrO8/7Q5DixES9W9hcOjrv55CRsrxfC1xwVOpMhQLOH3u+vuN1+XLe542x4Pj3DSoBcFgKzJDC0lAQURqB0iTSQws9kjSpgC570POEEA5A9ChC7vMIj82juBwuxeFyyjbZIVthly2Sd8rQkuSQLckO2ZrulG0ZbuHinyiYH5TxHvuG8R775sPalGUdWsOMeX6fqdEV15T9aPP8reXzz32PAN2sBrFnyySaee57K622QZcWEAJ9t7m/d/i/yhze542x4OkLVq3Rm01Gl9qFqI3DUmAy1S4gUESUBFCShqQRGuhg0CQj1Y+PcaG+LJdslx2yVThkK9nlPq3dY9HbvSErzSHbMlyKPRPcl5WA5BKPbW2Jx1a5T5d6hU2jn1Tmz1n25Lz59RO/vH7ygT8tO/e9hXtFz5ZJ55/z6T7ruItdUxHUfqPrp201YtJ5QYsxFrA0AAE/UBFvOCwFRrWRpUjjviwWHPcUt/VfcNvW7dSlfpI0usLpg51xKv+yxTmd+7aNaNs+u//rU46J84ZONUKcvMTtvuD+jB4hnTC6HnMdEOOCWoOJMXaedHBY4rAUoIQJS8EaYl+W1duXJfv6spwOl+JwO2Wb4pAtsHusGrvcp+O+rBggHDPclnfhptRafdpVqZJ29IXDC5Fm75SvF6VXH2tKsbef2XsxzYGJWo9werR0Zk2kS1zuBgADbk/iFLqGFc6nUk4gjze7Zix0uG8JHJYClaF2AfGMiFIJlCqRBC10SNL4M5YVUF+WwSFbUxyyNc2p2DLcipP/e4absJa5+t4SJGVu0aVenSdphxcPeBxRdnXZ/a2LN95n1yjuZAAgIGm6WezaXkJnRqeMFuuAayRZRNK+xc5n87uQkROWz8FY4uL+VfgZlnyjBUYAIwDsB3BUCBHSvaNiBIfLKDTEvqweBXKvLDxW75Shw+XkvqxQI6H0XObq+5NCUu5GXdrVYyVNznl9R4rGMLFm9n0bL6t5ZOHp1xbtFZ3bS3xfCCGuslgvOfe8dpGxfanzmUusSOafgBkLPf5zBf+/+b8F4DiAxQB+AOB1AMvCVBNjEeHry8rVQJurIS30UhL8+SFqkL4sxRuyLFq7x6Kzy33J3tEsW4ZTtmUleF+WJJSOha7e1W7SjKzUp111CUnp+f0PsKWOXHig5IaKiYf/shQApprFmdG/JCEO5irKxP7HNyrDt1zuemqWG1p9ZD4CYwmHR5bgf1jKF0J8jojWCSEqiYi3C2AJi/uyhkwn5FNLnD0vOyTtuApd6qemkpRyZqXgE6OXLszp2l+X17G7NNOGSRpZuGUN6WY7nKcAnAlLO5WiqutcDy8UkHiUj7Hw8a8fIs75G5b2E9GrAEYR0YMA6sNYE2NxK2R9WcLlcMkOl1Oxy46PFyXV2uU+g90TM31ZSYrn2FJnz2/6JF3Jel3KlTNJSsoEkXb31FtHz9/y4KlkZ+fIKcfEnt2FNPU6i/XMT7hr5Znrv+G+d5mKtTOWKHhwBH6GJSHErUR0Lbz9SvuFEA+FtyzGWH/n9WUhGanasPVlpbsVR6aAiNRfkumK+/AyZ8/hLo1+SoU2ZeUcIl1e9Zz79y7euCpn0T65Y3eBcCy32S4VAuINeUXl/Z5blkWoNsYSHY/cwv8G78kApgDQAZhJRKVCiIfDWll0IrULYCwQQ+zL6vX2ZckWj+KyuxWn06U43L6QhYH6slyyLVOBEmz/ULbs2rdUdtW3agylNSJ5yfxts+7ZOv3AEyk5irJPr2DGM57PbnxOvn5pkNdnjAWOwxL8n4b7KwATgGNhrIUxFiV8fVkZBE2Gty9LH6K+LKtwyBaNN2RZkuyyxduXJdszZeH2zUmK4bJzx3DZWdfUmTQP6fnXdC/q+6v0I88ttW/KKxaF71MzxgbAYQn+h6U2AG/4hvQZY+yChtCX5fT2ZSl9HuG2ehSX/ZhywrZ/rKvrwxM/Tm6VRmgBVIS3esbYOWxqFxAN/A1LtQDWE9HrACwAIIR4LWxVMcYSjp1cvcc1HY3HpHZbu6Y7OWf0Ltv24UXuf9csmKRIUqna9TGWoPh7PfwPS7t9vwS4b4cxNkRueCwnpa6GRqmt+5TUbbCQY6wgMVqrdWiKirftLslry3pc+amtpSplDHkUDkqMqUdWu4Bo4G9Yeh3AzfA2ee8BsDpcBUU5DoqMBUiB4mml3sPHNO1tJ6QO6iZbvgylCIQZp49JS2s/VDJh64a0tM7ZtTQv43n5Ma22qj2T3ByUGFMZhyX4H5Z+D+AQgH8DuAzesPTlMNXEGIthXWRpPCZ1nDiuafd0kCXHDU8JCJMATDr7SKHk5x+qKSisM+h0zlIXdGMfxUPV9fKU2YaqlgPkVmap8gEYY/1xWIL/YalACHE6HH1ARBvCVRBjLHbY4Gw7rukwH5Paba1ST5odriIQxgMYf6FztFpnT0HhjroRIw4XSZKYBwD7MbnehAf0bkU3x1DVXE8uDkqMRQl+sAv+h6VjRHQ/gC3wjiw1hq8kxlg0ulCfEYA8f85PTe06UjJhS1N6evtsIiwFAAWS/CvctWEzFi2AAtlQ2byXXMrssH4QxlggeGQJg4QlInrA91szgO/Bu6HuDQBeCW9ZLJ79tvotfHRkC974/DPnvffP/R/BVPFbDE/NAQCsvuEJCCFwx3sPoddhweKCMty75JZIl5xw/Okz8o8Qw4cfrS0s2ibpdI5ZRCg6/U4TxpjL8bjVTilLIQsHByXGohKHJQw+snS6odkJ4AUAy+D9KXJ+GGuKZk61C4h1TT3N+Oue/yA3JWvA93scfbhn0c349KVXnnntN1vfwPWXXonrL70SK165Cd+ceyOykngj7FDyv8/IPxqNu298Qd32kSMPjpckZc657/8ZX6n8F64pA1EKZOE0VDXvIZdSNuQPwhgLNbfaBUSDi4YlIcRDRKSHt5n7Nng30J0lhKiLRHFRqAfAWLWLiGUPrn0Oq5beipdr3h7w/R5HH97e/W+8VP0W5o2djocuvxtfmHEVknVJsLsdkIUCg0YX4arjSzB9Rv5KTu5pLJmw1ZyZ2TLz9FRbfx3IbX4ATzR1U/YSAPAFpd3k5KDEWJTqUruAaDDYNNyjAG6Bd7mAJwA0A0ghogVCiE0RqC/a9KhdQCx7d9+HmJJXggnDCi54zLT8iZgzZjpmjZqCBb+5EbfM+RzGZo6ErMiY/9vP4aqJy5CsS4pc0THudJ/RMam965TUldQXYJ+Rv4YNa9xeVFQr6w222UQDh67/wLj5j7h5Eoi8wUgRLkNV8y5ynj/yxBiLGu1qFxANBpuGGwXgX77fX93vdQGAwxILyNrDm3GytwUVR6txpPMYVm97B1+b/ZmzjpmUV4Sc5ExoJA1Gpueh3dqFJK0BOcmZ2Hjbm7ji1a/h6903YHzWKJU+RfQKXZ+RfyTJYxs3blft6DH1oyXpwk+vWZDW/RAe3XeSxiz4uFjhMlS11JFTmRuO2hhjIdOhdgHRYLBpuJsjVUiM6Fa7gFj2wjXe5wWO95zCD//95HlBCQAeXvcCvjjjGpSOnIwTva0ozBmLn/z3GXxl1nWYM3oadJIWTo8r0qVHpdN9Rk1Su7tDsuS6hthn5K+kpL4TxSXVh7OzT84gwpKLHbsFC7a9iO+OUkhzflByyByUGItuMvj7HgD/lw4YEiKSAEAIoZzzOgGQhBCy72uDEMLpO14LQAGgnHve6XOFECL81Z+FR5ZCqLZpN2pP7MG35n3hzGt3XnYTfvifJ+GW3fjuwq8iKykdd8z/Eu7/4Gk4ZTdWFM/HJReZxotX4ewz8ldOTtPOouIae1KSZQ4RRl/sWCf09ifw0+oDmLwE3j/nXopwG6padpBDnhf2ghljQ9VlNhnP+/6biCgSeYOILgfwA3gXt8oFkAngCAAJwHohxJO+47YB+JTvtBcB7AfwbyHExnOuNw3AswCuEEIovt6qD4QQleH8HE2rqh4D8KNw3oOxi/QZRRyR7Bw7dk/NmLF78zQaeaI/5+zDpfuewE+TPaQrPOsNRbj1VS3bJQ5KjMWKA2aTMawj1bEi7CNLvqfpygCs971UBGA0gNOrgEtEVARviDoI7x50nwQwAcAsAFcQ0XEhxGdPX1MIsZuINgMYR0TdAK4B8LNwfxbwcCQLsUj3GflLr7c2l5RU78/JbZpKhEX+nCND8ryA722oxvxFIDr77xZFePQbWrZJDvmysBTMGAsHbu72icQ0HAFYiI9HZOYAuBTAP31ffw5AAYA7AbwH4E8Afgfg5/AuhvlPAPeeuRjRcwBmwNtkvgjAMADZAP5DRHYhxCfD+Fm40Y0NiVp9Rv7Kyjq1p7ikujc5uXcOEZb5e95xjDtajkftDko5/xxvUKqV7ByUGIsx/D3PJxJhSYJ3m5SbAcwG8Dy8IzQvwLsUweu+16fBG5YeBLAUQAkAB7yjTFK/6/0QgOsC/U/hfqa8LczXZ3EkGvqM/EGkuEeP3lc9dtyeLK3WPTWQcwUg/oibKz+AcS6Iks87wBuUaiS7nKgL2TIWyzgs+YQ9LAkh7AAeJaKvATgOb5N0LQCL75A18Ian0/9RLgVwK7w9Tk0A/imEqO13PccF7iMA2MPwEfprDfP1WYzq12fUfUrqMoRrPaNQ0unsbcXFNXuH5R2bTCQWBnp+G/JOPQjTyR7KOm/xSQCAELJ+IwclxmIYT8P5ROppuHnwrgLuAHD5Ofd3CyFeJKLhANLg3aT3TwByfMd8kohyABiFEAeJaDq8o1Hn/kfMBHCrEKImjB+FR5ZY1PYZ+Ssjo7W+pGRrR0pqd0BTbf39E9dufAM3TQXRwHu5CSHrN7RulWzyggHfZ4zFAh5Z8olIWAJQB++0m+ec16cAGNn/BSHE94joDnhHmybA27P0TfhGooQQuwAENFUQQjyylIC6yNp4XGo/cVzq8HRIfTkueIqjqc/IP4pn1KiDNePG70zV6VzTg71KH9K7yvHY/mYadeGRKCFk/cbWrZLNw0GJsdjGYcknUmFJA29Qspzzuh1ARr+vJd8I000AVsLb2L0Q3kbuFt/6SzohxIAb2vqevFOEEOeGspAYY1rc17SqyoHw90YxlcRKn5G/tFpHZ1HRtl3DRxy9hEgMaTpsExbV/gp3jRGkufB1vEFpi2T1BDytxxiLOjwN5xOpsDQTwF0Azl16ORtAVb9acuGdYrtLCGElIh2AHQBmCiFkIhoL4G0iGjAsAUgFcDu8PVHh0gbeTDcuxGKfkb/S0toPlUzY2pyW1lkW7FTbaU4YbI/jwdpDNPGiq3VDCEW/iYMSY3GER5Z8odeLBQAAIABJREFUIrIoZTxpWlVVDe/yByyGXKTPSBr87FghlPz8Q7UFBXU6nd45MxRX3Iupe5/ET1I9pCu4+K2Fot/UukmyePxak4kxFhOmmE3GerWLiAaRGlmKJw3gsBT14qPPyD8ajaunsHB73Yj8w0WSJEKy35oHGvfzuGdjLeYuBpHmogdzUGIsXnGfrg+HpcAdVLsAdrZ46zPyV0pK19EJE7YeT89om02EgR/fD0IjChoewiMuJyUvG/RgIRT95jYOSozFn06zycjTcD4clgJ3SO0CEpkbHutJqetwPPYZ+UeI4cOPbiss3A6d3j6bCIWDn+PnlQGxGrdU/g//Nw9Egz/EIITQb27bKPW5F4eqBsZY1DigdgHRhMNS4HhkKULO7jPqRDdZY2o9o1DSaNyW8ePrto8cdXCsJCllob5+G/JO/hRPtPRRpn8jVEII/Za2DRyUGItbHJb64bAUOB5ZCpNush47JrU3JUKfkb+Sk3saSyZsbczMbCklwsWfRgvSP3D9xrfxxakg8q8p/HRQ6uWgxFgc44GBfjgsBWiMaXFX06qqdng38GVBOqfPKNUOVzEI4wCMU7u2aDBsWOOOwqJat8FgKyMKT+9VL9I7H4TpYCvl+/+ovxBCv7WtSup1hyW4McaiBo8s9cNhKTiHwGHJb9xn5B9J8tjGjdu1bdTo/SM1Gjkkj/5fSBWW1ryEO8cLki7z+6TTQamHgxJjCYDDUj8cloJzCABvDjoA7jMKXFJS34nikurD2dknpxMhrFNbDhisj+Kh7UdoQsD30Ve3c1BiLDEoAA6rXUQ04bAUHJ7L9eE+o+Dl5DTtKiqusSUlWeYQYXS477cLM3Y/hfszZNIGHpS2tlVI3a6QLU/AGItqZrPJeKGdMhISh6XgJGSTN/cZDR2R7Bw7dk/NmLF78zQaOegNbQPhgcb9LO7duANlgy8wOQB9NQclxhIMT8Gdg8NScOJ+ZIn7jEJLr7c2F5fU7M/NPT6VCBFbwPEoCg//DI/ITkpaFsz5uuq2CqmLgxJjCSbuv8cFisNScOJqZIn7jMInK+vU3uKS6p7k5N45Q93QNhAKSPk9bq1ahysuA5EhmGvoatoqNByUGEtEPLJ0Dg5LQRhjWmxtWlV1EsAotWsJBvcZhReR4h49ur5m7LjdmVqt+9JI378FI5oegKndQhlBBx1dTXuFppODEmMJisPSOTgsBe8QYiAs2eBsa9J0HD0mtdtbPt43jfuMwkCns7cVF9fsG5Z3bBKRWKBGDX/DDRvewY3TQTQm2GvoatsrNJ1ODkqMJS4OS+fgsBS8A0DoNi8NhVjtM1IUBe+88w76+vqQm5uLa6+9dsDj/v73v6O9vR2pqam48cYbcerUKbz11lvIysoCAFxzzTUYNizyy19lZLTWl5Rs7UxJ7S4L5Ya2gehBZvsDMDW00/Ah9UPptrWv13Q4l4WoLMZY7LGYTcYTahcRbTgsBW+nmjdXoHjafH1GTVIndZN1RKz2Ge3fvx8jRozADTfcgD//+c9obm5Gfn7+WcccO3YMiqLglltuwerVq9HQ0ABJklBWVoYlS9RY+keRR406WDNu/M4Unc4VkafaLmQ9VlS/gtsLBUnzhnId3bb29Zp2DkqMJThu7h4Ah6Xg1UTyZvHcZ1RSUoIJEyZAURQ4HA4YDOf3I6empmLePG8WEEIAAOx2O+rr63HgwAFkZmbihhtuABGFtVat1tFVVLRt1/ARRycQCf9Xvw4DO5L7HsFDdWYqHvJClrrtHRyUGGMAsEvtAqIRh6Xg7QTgAqAP9YUTrc9Ir/f+K3zllVeQlpaG7Ozs847Jzc0FANTX14OIUFxcjObmZixfvhyXXHIJfve736GxsREFBQVhqTEtreNQyYStzWlpHapNtfVXh5m7foFV2cEsMHku3Y6O9Zo2x7IQlMUYi30b1S4gGnFYCtIY02JX06qqnQDmDOU6sdpnFEo2mw16vR5f//rX8dprr+Ho0aMoLCw877gDBw6guroaX/jCFyBJErKysjB8+HAAQFZWFqxWa4grE0p+/uGa8QU7DHq9sxTAhBDfIGAeaF2/wH2bdmLmEhBJQ72ebkfHek0rByXG2Bmb1C4gGnFYGppqBBCW4qnPKJQ2b96MvLw8TJ8+HTqdDh6P57xjLBYLNm3ahC996UtnRqI2b96M3NxczJgxA62trSHrXdJoXD2FhTvqRuQfKpQkMaQ+oFBqQMnBR/AwXGRYForr6eo4KDHGztIFoF7tIqIRne7/YIFrWlX1VQCrL/T+BfqMkiNXYWzo7e3Fu+++C4/Hg+zsbCxbtgy1tbW48sorzxyzYcMGbN++HWlpaQCAmTNnoqSkBO+88w7cbjdKSkqwfPnyIdWRktJ1tGTC1uMZGW2ziJA2pIuFkAJSXsHtlRVYsQBEIZn21e3srNA021WfTmSMRZU1ZpPxKrWLiEYcloagaVXVZAD7gDN9RuZjUrutX5/R+c03LMoIkTf86LbCwu2k19tnESG8HeIBOoWRxx/E451WSg/Z6KNuZ+d6TbN9WaiuxxiLGz82m4yPqV1ENOJpuKE58I5+y9pusk0UJMYggfqMYp1G47aMH1+3beSog+MkSSlTu56B/BU3Vr2LG0pBNDZU19Tt4qDEGLsgbu6+AA5LQzDGtFjpKl/rARD0asksspKTe46VTNh6NDOzZWY0PNU2kG5ktT0A09EOyhvyk2796XZ3rtec4qDEGBuQG94+XDYADktDtxHAJ9Qugl1c7rDG7UVFtR6DwVZGFL1LMKzDFVtfxa3FgqS5obyudnfXes1JDkqMsQuqM5uMdrWLiFYcloaOhy2jlCR57OPG7a4dNbp+pEYjz1K7nouxIaX3Z3h41zEqHNJ2JQPR7ularz1pWxbq6zLG4gp/L7sIDktDtwWAB/zvMmokJfWdKC6pPpSdfXIGEUI6lRUO21BW9yx+OEwhTeiD0t6uCu0JDkqMsUHx+koXwU/DhUB5eXkNgKhsEk4k2TlNO4uLa+xJSZY5RNCoXc9g3NA6n8L9W/Zg+uJQLDB5Lu3ergptky0q+7IYY1FntNlkPKl2EdGKR0NCYy04LKmCSHaOGbu3ZuzYvcM0Gk/MLO55CJcceBTlkpsMYQkz2n3dHJQYY/4yc1C6OA5LofEBgPvULiKR6PW2luKS6vrc3ONTiRDy6atwUSDJL+GOqg1YGrIFJs+lre+u0B63clBijPmLp+AGwWEpNDYCsAJIVbuQeJeZdWpvSUl1T3Jy7xwiLFO7nkCcxKjGB2HqtVHqsnDdQ1vfXaE9xkGJMRYQbu4eBPcshUh5efn7AHiZ+DAgUtyjRtdXjxu3O0urdV+qdj3BeAtfrHoP188CUdgCtXZ/T6W20RKaDfIYY4lkhtlk3KV2EdGMR5ZC5wNwWAopnc7eVlRcuzcvr3EykViodj3B6EJ26wMwNXbSsLA+lac9wEGJMRaUYxyUBsdhKXQ+ULuAeJGe0bq/pKS6IzW1qyzWptr6+xD/t+UP+MYEQdKccN5He6CnUmO2RP0SCYyxqPSe2gXEAp6GC6Hy8vIjAArVriM2KfLIUQerx4/fmarTuaarXc1QWJHS8zAe2dNE48M+GqY92FOlOWpZRIiuDYAZYzHjCrPJ+D+1i4h2PLIUWh8A+JbaRcQSrdbRVVi0feeIEUcuIRLz1a5nqGowb8dzuGeEQprwB6VDHJQYY0PSDaBC7SJiAYel0OKw5Ke0tI5DJRO2NqeldcT0VNtpLugcP8ePt+7D1CUgCnt40R7qrdIc4aDEGBuSf5lNRrfaRcQCDkuhtQ689clFCGVE/uHagoIdOr3eORPABLUrCoUDmFT/OB7Uu0kfkUf2NYd7N2iO9HFQYowN1T/ULiBWcM9SiJWXl1cA4KeS+tFoXD0FhTvq8vMPFUqSGDeUa8mywKOPtKKjw4MxY3W4997hAx7n8QiUP9iCRx7NBwDY7Qoee6wVvT0yLr00CbfeljuUMgB4F5j8Fe7asBmLFoBIN+QL+kHT0LtBe7hvAQEh3x6FMZZQXACGmU3GPrULiQX8F27ovat2AdEiJaXr6PQZ/6mcv+AtzahRB5cONSgBwMYNVhQV6/HL50ajs0PG4cPO845xOhXc/q0T2LbNfua1tWstmDzZgF8+NxrmRhcaG11DqqMJY8y3YnX9Zlq8lIMSYywGfcRByX88XRR6fwPwjNpFqEeIvOFHtxUWbodeb59NFNqnA+fMTcHced4RJotFQUrK+bnBYJDw8itj8JWbjp15LS1NQmuLB7Is4HIK6HTBz2C9jpsq1+DaMhAVBH2RAGmO9G3koMQYCyGeggsA/8UbYuXl5ccA1KhdR6RpNG5LYVFt5cJFr5snTdpYZjDYy4hC31OTnCwhKUnC3XedRHa2BqNG+Teos2hRKmpqbPjKTccxbpze7/P660Bu8x14uXYNXbcERCkBXyBImqN9G7WHeudzUGKMhYgAr68UEP7LNzzeUbuASElO7jk2bdqHFfMXvCmPGVO/RJKUsK4z1dMjw+US+OVzo2CxKKjbYR/8JABvvN6Nq6/OwJ9fH4e+Phl79zoCuu9/YNx8F14ydFNOWTB1B0tztG+j9mDvZRyUGGMhtM1sMp5Qu4hYwtNw4fEOAJPaRYRT7rDGHUVF29wGg7WMCEPuRfLXX//Sg/Hjdbj8inQYDASny78HFGx2BSP13v/ddTqC3a74dZ4VqT3leHTPSRob8e1WNEf7NvmCkibS92aMxTWeggsQ/7QaBuXl5YcB7FC7jlCTJI99fMGOqgULXz80ZUrlzKQk61yiyP4/dO21GfjPf/rwnTtPICNDg7FjdXjpNx1+nff++734zp0n4HQJzJyZPOg5WzF/+7fwe5sqQcls2aQ92DuPgxJjLAw4LAWIlw4Ik/Ly8nsBPKl2HaFgMFhOlpRUH8zOOTGDCNlq1xNuTujtT+In1fsxJSILTJ5L02jZrN3fM5eDEmMsDI6aTcYitYuINVE5DUdEUwHsF0J41K5lCN4C8ARieOHA7JymXcXFtdakpL65RBildj2RUI8p+0x4INlDuogsMHkuzTHLZu3+njkclBhjYfJ3tQuIRVE3DUdEEoA1AEqI6DgRrff9qiOinw1y7qtEVNrv6zQietz3e63vn+/5/kkUxlED31Nxm8N1/XAhkp1jx+3auGDhG/VTp340PTm5bz5R/H/jliF5nsP31z+Chy/xkE6VzZA1xyxbtPU9cyhKf4hhjMWFP6hdQCyKxr+UlwKoFkLsJ6KTQohlAEBEywAsIyINgE54e4JKAeQJIU7vbeMC0H+fGw+AkUQ0AsAfiEgGUEpE/4R3xOeHAPaG8bO8CWBBGK8fMnq9raW4pHp/bu7xKUSIeI+Omo5j3NFyPGp3UMoytWrQHLdu0db3zOagxBgLo1qzybhT7SJiUTT+xfxDAFt9vx9ORH/y/X4EgC1CCJmIdgshlhHRegBf9k3buQDMBnAPEb0AQAdv8JoC4DJ4V9ZOBzAKwHp4+7XCGZQAb1h62ldLVMrMbN5bUlLdnZzSM5cIqkw9qUUA4o+4ufIDGOeCaPCO7zDRHLdu1e7rnk1R/P8JYywuvKx2AbEqqsISEX0RwLB+L00CMAZAOYCr4R0pAoCpvqBUCmA5gLFCiGNElA7gBSHEft/1dsIblnYD+D68C3GN9l1rEhF1CCFeDdfnKS8vbysvL/8HgM+G6x7BIFLco0bX14wbtztTq3VfqnY9amjHsFMP4ImTPZSlakDUNFmrtfu6Z3FQYoyFmRXAG2oXEauiKiwBaAXwIIC5RLQG3vqyABTAu9qojohWA9jZb2QpBcAaIpozwPUmAFgEb6h62nfsXQBeAnAVgNVh/CynvYwoCUs6nb29qLh2b15e40QiERPTg+GwBtdseh1fmQKi2WrWoWmyVmv3dpdyUGKMRcBbvBdc8KIqLAkh/kdEi3y/NxLRTAAPwRtyKoUQjwAAEd3S7xwrEb0KYHr/axHRN+GdhtsqhPgbES0HcBJAH4CDAH4qhPBvZcKh+RCAGd7Ap4r0jNb9JSXVHampXWWJNtXWXx/Su8rxaH0zjVY9KEonrDW+oKRXuxbGWEJ4Re0CYlnUrbPkC0uXw/vY/YcAvgfgDngD0x+FEO8TUQ8+bvDOFULIvnNfwNnTcEkAfiOE+BoR7QVQBu+U3B4AyQCuFkIMbft5P5SXl/8UwMPhvs/ZFHnkqIPV48fvTNXpXNMHPz6+bcKi2l/hrjGCNPlq1yKdtNXodndNJ8Cgdi2MsYSw12wyTlW7iFgWVSNLPgQgA97lA14AcMr3+rcA/IuIigCcbvC+d4Bz+5MACF8DeAuA+wBsE0LcGLbqB/YqvNOLYX8EX6t1dBUWbd81YsSREiIxP9z3i3ZOGGyP44HaQzRpidq1AIB00lbLQYkxFmHc2D1E0TiytALAFQDWCCE2EFEhgEeEEF/yrZWUDCBbCHHsnPNuAXA3gKVCiE7y7gr/d9+vufCupq0H8GMAIwGkAnhICBGRBbrKy8vfh7dPKixS0zoOTyjZeiotvWM2EVLCdZ9YshdT9z6Jn6SotW7SuaRTtlrdrq5pHJQYYxHkBDDKbDJ2ql1ILIu6sBQsXzhy9O9DIqJsIUQXESULIfzbnj5MysvLr0XIV04VyogRDbUFhTu0er1jVmivHbs80Lifxz2bajF3EbzrcqlOOmXbptvVdSkBSWrXwhhLKG+YTcYvql1ErIvGabigCCFsA7zW5funqkHJZw28U4ojh3ohjcbVU1C4oy4//3CBJClzh15a/GhEQcNDeMTlpOSoaWSXmjkoMcZUw43dIRA3I0uxoLy8/FEA9wd7fkpK99GSCVuOZ2S0zSJCWghLi3kCEH/ANyo/xCfnwdvYP6Ae008hH28EZWUj6+GnQZqzf15Q+nrR/cD3AY8H+rkLkHbTrWfes/7lj3Bt3YDsp17yuy6p2b5dt7NzMnmnjxljLJIaAEwwm4z8jX6Iom5vuDj3CoAAlysQIi/v6La58/5aO2v2+wWZmW1LOCidrQ15J2/Hq3Uf0qeWXiwouXbvAGQZOS++BmGzwlVz/tZ9jrX/hragGDnPr4Z7z07Ip04AAOTmk3B88H5AdUkt9h0clBhjKnqFg1JocFiKoPLy8qPwLq45KI3GbSksqq1cuOj1o5Mmb5htMNjLiM572i/h/QPXb/wufp3aR5kzBztWys5ByvW+qXvlAplVCAibFUIIQAi4Dx8AAPS9+HOk3fIdv+uSWuw7dHWdEzkoMcZU4kFkFl5OCHHTsxRDfgHgugu9mZTce7ykZOuRrKzmUiJExePu0agX6Z0PwnSwlfL93vRXO2Y8AMBRtQ6QJOjnnL+yQtIVRjhrN6PnwR8AOh2Eywn72n9DW3wJtAXFft1HarXX+YISP5XIGFPLe2aTsVntIuIF9yypoLy8vBrAWduz5OYe21FUXOs2GKxlRDzidzFVWFrzEu4cL0gaHui5jo3rYXvnz8h65FlIKannva9Y+gCPG1JWDrrL70Xy1Z+B49//gNzaDMgyPMfNSLv520j59OcHvL7Uat+p29FZQt6lKRhjTC1zzSZjjdpFxAseWVLHMwBelySPfey43bWjR9fnazTyoNNIic4Bg/UxPLS9gSYsDuZ8ubMdtrdfQ7bpRVDywLNjrp3b4Pjv+8j88ePwNByAbsp0GGZf5j2/+SR6n3rowkGpzcFBiTEWDT7koBRaHJbU8ZeJEzd8Lm/40SVECOobf6LZhRm7n8L9GTJpg/735fjgfSgd7ei679sAgOT/uwaexqNIv/37Z44xzFsI+z/fQefdX0fqTd+ElOzfTJrU5til295RzEGJMRYFHlW7gHjD03AqWbuu+LvwjjCxi/BA434W927agbKoWWDyXFK7Y5duW0cRgZ9SZIypbqPZZFykdhHxhntj1PMyAF5+/iKOovDwrXjtyA6aszSKg9Ju3baOQg5KjLEowaNKYcAjSypau674YQA/VbuOaKOAlN/j1qp1uOIyEEXtPmpSh2OPrrZjHHk3fmaMMbVtN5uMs9UuIh7xyJK6ngcQDVuxRI0WjGi6Ha/uWkdXLo3yoLSXgxJjLMrwqFKYcFhS0coVDW0Afqd2HdHiXXx24/fxYoaFMkrVruVipA7nXl1txxgOSoyxKLIPwLtqFxGv+Gk49T0O4BtI4JWee5DZ/iAeP9xGI/xeYFIt1Oncp6ttH0NAptq1MMZYP4/z1ibhwyNLKlu5ouEkgF+pXYda1mNF9R14RbTRiMvUrmUw1OWs19e0j+agxBiLMkcAvKF2EfGMR5aigwnAbUigJ6rsSLI8god3mKk4JtaZoi5nvb66fSQHJcZYFHrCbDLKahcRz3hkKQqsXNHQDuBZteuIlDrM3HUb/tAZQ0Fpvy8oZaldC2OMneMEeMPcsOORpejxFIA7AGSrXUi4eKB1/QL3bd6JmYtBFBNBnbpdB/TV7SM4KDHGotRTZpPRpXYR8S4mvmElgpUrGnrgDUxxqQElh76J18w7adbSmApKW9vyKI4DLGMsprUB+K3aRSQCHlmKLr8EcDeA4WoXEioKSHkFt1dWYMUCEOnVrsdf1O066AtKOWrXwhhjF/Cw2WS0qV1EIoiJn/ATxcoVDVZ4lxKIC6cw8vi38PvdFbRyWUwFpR7XIf3WtlwOSoyxKLYXwK/VLiJRcFiKPr8G0KR2EUP1V9xY9QM8n2Wl9Blq1xII6nEd0m9pyyYgV+1aGGPsIr7LT8BFDu8NF4XWriv+BoBX1K4jGN3IansApqMdlDdX7VoCRb2uw/rNbVkEDFO7FsYYu4j3zCbjtWoXkUh4ZCk6/R5AndpFBGodrth6J16mGA1KDfrNbZkclBhjUc4F4B61i0g0PLIUpdauK14KYL3adfjDhpTen+HhXceocJHatQTDF5QyCMhTuxbGGBvEU2aT8V61i0g0PLIUpVauaKgA8De16xjMNpTV3YbVvTEblPrcR/Sb29I5KDHGYkArgJ+pXUQi4qUDotsPABgBGNQu5FxuaJ1P4f4tezA9ZhaYPBf1uY/qN7WmURwt1cAYi2s/NpuMvWoXkYhi8ptcoli5ouEoonChykO45MA38dqxPTQjZhaYPJcvKKVwUGKMxYgdAF5Vu4hExSNL0e8xADcBGKd2IQok+SXcUbUBS2NqgclzkcVt1m9qTSZghNq1MMaYn+42m4yK2kUkKm7wjgFr1xV/FsBf1KzhJEY1PghTr41Sp6lZx1CRxd2o39iqJ2Ck2rUwxpif3jabjDeqXUQii8kplESzckXDXwH8T637v4UvVt2L53I5KDHGWMQ5APxQ7SISHU/DxY7vANgJIGLTX13Ibn0ApsZOGrY4UvcMF7K6j+k3teo4KDHGYsxTZpOxUe0iEh2PLMWIlSsa9gN4NFL3+xD/t+U7+K2mk4bNidQ9w4WsnuP6ja0aEhildi2MMRaAEwBMahfBEmRkibzNyB4hxKDNceR9umsygH3Cj4YuIkoGoBNCROJxzscBfAbA9HDdwIqUnofxyJ4mGr8wXPeIJLJ6mvQbW4gERqtdC2OMBeh2s8loVbsIljgjS08BqCKi9f1+7SOinw9wbC6Ady90ISL6GRH1n5aaA+DJENc7oJUrGtwAvg4gLJsn1mLujm9htSVugpLtTFAao3YtjDEWoD+bTcb31S6CeSXEyJIQ4i4iWg7AIoSoAQAiuh39wiIRFQH4LYBkAOkAPiSi02//6PR5AN4E8ASAqtOXB9AR9g/hs3JFw7a164qfAnBfqK7pgs7xc/x46z5MXYJ+HzqWkc1zQr+hBTyixBiLQc0A7lK7CPaxhAhLPpcD2NTv6xEAtvX7OglAkxDia/1PIiITvOEJACCE2EtEnyai2wB8Et5FDdOJaBqAFACfEUL0hOcjnFEO4NMALhnqhQ5gUv3jeFDvJv3SIVcVJcjuOanf0KKQwFi1a2GJQSgy2t97ErKlE9qc0UibugLdlX8CAHh6W5G1+CakTVs54Lnta34Bd8cJaFIzkffpH4MkDQCgt/pd2I/UYsTnI9aqyKLH7WaTsVPtItjHEmUaDvBOr53s9/UIePfZOU0BcCUR/a//LwCf970HIlpBRJUAHhJCvCSEuA7Av+ALL0KIyyMQlLByRYMDwDfgHdUKigJJfhF3r38Yj5S4SV8cuupUZvec0le1eDgosUiyHdwM3fBC5H/555AtXZAMacj/8pPI//KT0OcVQD+iaMDzHE17IRQZI7/yNBSnDY6j2wEAnp5WWPasjeRHYNHjTbPJ+He1i2BnS6SwNBbAb073LAG4HsDzRPRKv2P+6ws8Z37BO+0GABBCrAPwFQBFAEBEGngbrjMAvBShzwEAWLmiYQOAF4M5twljzLdidf0mWrIMRLoQl6Yeu+eUoarFRUL91c5ZYkkumo2MOddBKDIUpwVkSAEAKG4H3N2noB9eOOB5mpQsZMy+xvtFv+dJOtf+FtlLvxr2ulnUaYV3mRgWZRJmGk4IYTz3NSIaA2+f0mlX+kaT+isB8J8BziUAv4C3GXw1gBuJ6BtCiN+FrOjBrQJwFYACf094HTdVrsG1ZSDy+5yY8HFQGq92KSzxSPpkAMCp174PTVoOdFn5AACHuQ5J42dc8DxdjrelznZwE0CEpMJZsO5bD31eIXTDOPMnoG+bTcZ2tYtg54v7kSUimkxEG86dXiOijQCyAZzqd/hFR5bOcRuA8QAe9S0x8FV4A1PEGqRXrmiwAvimP8d2IqflDrxcu4auWwKilDCXFlkOT7OhqsXJQYmpRbb3QnjcyP/yz6E4LHA07gIA2A9XI6X44kuV2Q5tRe+29zH8Mw+AJA1sh2vgaNyJtn88CVfzYfRu4weiEsQfzSbjO2oXwQYW9yNLQoh6AIvOfZ2IkuCdimvr9/JFR5Z8azCtAnAZgA8A3CSEkH33aQFwZcg/wCBWrmj439p1xS8AuPMqC+s0AAASBklEQVRCx/wHxs1/xM2TQFQWwdIiwyG3GCpb7CQw8DwHYxHQW/0udMPGIe3S5SCtAcLjhBACjmO7kXPFty54nmzpQm/1Oxh+w8OQ9EkAgLxr7gUAeHpa0PHv55Ax++qIfAamqkZc5O9wpr64D0sX8Qi8T7N9w/e1Dt6Rpa/1P8j3NJzB92UpgD54n0K7B8AHvgUvJXj/XWYA+JYQ4rxpuzC7F8AyAFP7v2hFak85Ht1zksbGxbpJ53HIrYbKZhsHJaa29FlGtP/zF+jb/k/oskYiqXAWXKcOQjdsLEjr3aHI3d0My/Y1yF7xjTPnWfashWzpQuvbDwAA0qZfjrTpEf+Zi6lLAfAVs8kYiYWNWZDIj0Wq2SB8jd4aAPLpkaZIW7uueCqAGniXQMBWzN/+Ar43UiFNfO6F5pTbDJXNfaRg4MeMGGMsNvzcbDLyRrlRLu57liJBCCELIVxqBSUAWLmiYQ+AHzqht/8MD1c8h3tmxnlQ6uWgxBiLcTsB/ETtItjgeGQpzkxYV7G6jzLj95njj4NS/KwNxRhLRE4AZWaTcY/ahbDB8chSnOmjzHsANKldR1g45XZDZXMPByXGWBz4Hgel2MFhKc40Ly/tAPBFhGmzXdU45Q5DZXMXKShRuxTGGBui35tNxl+rXQTzH4elONS8vLQK3i1Y4oNT7jBUtnSQgglql8IYY0NUC+B2tYtggeGwFL8eA/BftYsYMpfc6Q1KYsibBjPGmMraAFxvNhmdahfCAsNhKU41Ly9VAHwBwBG1awmaS+4yVLS0cVBijMUBD4DPmU3G42oXwgLHYSmONS8v7QTwaQBWtWsJmEvuMlS2tJAiJqpdCmOMhcAPzSbjerWLYMHhsBTnmpeX7gJws9p1BMSldBsqW5pJFpPULoUxxkLgDbPJ+IzaRbDgcVhKAM3LS/8C4Am16/CLS+k2VDafIllMVrsUxhgLgZ0AblG7CDY0HJYSx/3wbv4bvdxKj6Gy+SQHJcZYnOiEt6HbpnYhbGg4LCWIfg3fDWrXMiC30mOoaD5BspiidimMMRYCCoAvmk3G2H3Ihp3BYSmBNC8v7QJwHQCL2rWcxa30GiqamzgoMcbiyE/MJmN0j+Yzv/HecAko/6O6TwP4K6IhLLuVXkNl8zHyiKlql8IYYyHyN7PJ+Bm1i2Cho/43SxZxzctL3wVwr9p1wKP0GSqbGzkoMcbiyD4AX1O7CBZaHJYSVPPy0l8AeEG1AjxKn6Gi2UweMU21GhhjLLSOA/g/s8nYp3YhLLQ4LCW2uwG8F/G7ehSLoaL5KAclxlgcaQdwJa/QHZ+4ZynB5X9UlwLgIwBzI3JDb1A6Qh4xPSL3Y4yx8LMAWG42GWvVLoSFB48sJbjm5aU2AEYAh8J+M49iNVQ2N3BQYozFESeA6zgoxTcOSwzNy0vbAXwCQHPYbuJRrIbKlkPkFjPCdg/GGIssGd61lNaqXQgLLw5LDADQvLz0KIBPAQh9Y6Ks2LxBSSkN+bUZY0w93zKbjH9TuwgWfhyW2BnNy0t3ALgKQOiW5peF3VDRcoCDEmMszvzIbDK+onYRLDI4LLGzNC8vrYR3lW/nkC8mC7uhsrme3MrMIV+LMcaix1Nmk9GkdhEscvhpODag/I/qjADeBaAL6gKycBgqm/eRS5kV0sIYY0xdq80m481qF8Eii0eW2ICal5euAfB5AJ6AT/YGpb0clBhjceYfAG5RuwgWeTyyxC4q/6O6LwD4E/wN1h8HpdlhLYwxxiKrAt7VuR1qF8Iij0eW2EU1Ly99A8A3AAyeqmXhNFQ17+GgxBiLM7UAruGglLg4LEUJItIP8r6GiLSRqqe/5uWlqwHcftGDvEFpNzmVsogUxRhjkVEJYKXZZOxVuxCmHg5LEeYLPZLv9//r99a/fa9JRKQZ4NRCAH8gom8S0SciUOpZmpeXvgTgTgw0wqQIl6GqeRcHJcZYnPkXvFNvHJQSHPcsRRgRfQHA1wC44d2Prdr31hwANQA0AF4DMBrAcnwcTnQApgHYDiAVwNeEEI0RK9wn/6O6mwG8gtNBWxEuQ1VLHTnkyOwtxxhjkfEmgK+YTUa32oUw9XFYUhERrRJCmHy//74Q4hf93vstgDcA9MAbmNoB/FoIcRURlQghDqtSNID8j+o+D+CPUITCQYkxFod+C+B2s8moqF0Iiw6q9MAkMiLSATgJYKfv62sBEAALEX0KwKVCiJHwjjBNAXAjgM0APvAdXwLgVQBLIl+9V/Py0jfzP6qz6ze0/IAc8iK16mCMsTB40mwy3qd2ESy6cFiKPAXAhwC+JIQQRHQjgFQhxKtERAD+7DsuCcAXAFwC75SdDoAFwDUAXo582WdrXl76j4IP1vQB+DuAdLXrYYyxELjfbDI+rnYRLPrwNJwKiOhH8I4MDQMwFd5RpM3w7sn2PyHE00T0LwAPAbgVwEcAhgOwA7gP3tEnqxq1n6tg1ZrZ8Dan56ldC2OMBUkAuMNsMv5a7UJYdOKn4VQghHgcwE0A+gCUA3gcQCuAzwkhnvYdlgXgOLwraL8E4BS8fUsnAMhEZIhw2QMym4zbACwGcEztWhhjLAgeADdxUGIXw2Epwogom4i+DeB9AN8B0ADgKIBnAHxARLcT0WwABwAsBVDs+6cewN0A6gD8BcB3VSh/QGaT8QCAhQD2qV0LY4wFwAngM2aT8c+DHskSGoelyCMAmQCuEELsBWAAoBVCbALwKXj7f1IA/AbAEQBXCyFqAcwG8FV4A9MGeEeboobZZGyCd4Rpq9q1MMaYHywAPmU2Gd9TuxAW/bhniYVUwao1qfDuJXed2rUwxtgFtAG42mwy8g93zC88ssRCymwyWgFcD+Bn8Gc/OcYYi6ztAMo4KLFA8MgSC5uCVWs+A+AP8K44zhhjansDwDfMJqNd7UJYbOGwxMKqYNWaaQD+Ae/edoyx/2/vXmPsKAs4jD8DpUCRoqCiFHEwhItARKKoMXITLWbUkJBiCKAELwSEgBhwhAQVNE4FIwiGWE2DiAERhBgnqfqh4hUJSBCpSAiMSqPhJhWoBdp9/fCebZcKL7vdy3suzy852XK22/y/7cN75sxRDmNA3TXVJbmHaDAZS5p1Zd3uAtwAHJl7i6SR8yRwfNdUK3IP0eDymiXNuq6pHgcWA1fk3iJppKwCDjGUNF2eLGlOlXV7CnAV8b5RkjRbfgKc2DXVU7mHaPB5sqQ51TXVcuBw4F+Zp0gaToH4btxjDCXNFE+WlEVZt4uAm4G3594iaWg8DXysa6of5x6i4eLJkrLommo18cOEv597i6Sh8CDwLkNJs8GTJWVX1u1ZwFLiR79I0lT9nPiOtydyD9Fw8mRJ2XVNdTlwCHBP7i2SBso64DPA0YaSZpMnS+obZd1uC3wFOIf4gcOS9FLuIr7bbVXuIRp+xpL6Tlm3RwBXA3tkniKp/4wRX7b/QtdUz+ceo9FgLKkvlXW7E/At4ITcWyT1jYeAk7qm+m3uIRotxpL6Wlm3HyHexPJVubdIymo5cLb3TlIOxpL6Xu+eTN8D3pt7i6Q59yjwqa6pbsk9RKPLd8Op7/XuyfQ+4rte1mWeI2nu/BQ40FBSbp4saaCUdbs/cC1wUO4tkmbNM8A5XVMtyz1EAk+WNGC6proXeAfQAOszz5E0824DDjKU1E88WdLAKuv2AOBK4LDcWyRN2xPABcCyrqnGco+RJjKWNPDKuj0euBTYLfcWSVM2BnwHuKBrqsdzj5FejLGkoVDW7Y7AhcBZwDaZ50ianNuAM7qmujP3ECnFWNJQKet2P+AKvM2A1M8eAWrg6q6p/CWkvmcsaSiVdbsE+DrwhtxbJG20gXhn/gu7plqTe4w0WcaShlZZtzsQLxj9LDA/8xxp1N0KnNk11T25h0hTZSxp6JV1uzfwTWBx7i3SCFoNnNs11XW5h0hbyljSyCjr9hjgMuCNubdII+B54BvAxV1TPZ17jDQdxpJGSlm32wGfBD4PvD7zHGkYjQHXA1/qmur+3GOkmWAsaST1oulU4jtyXpd5jjQMxiPpoq6p/pp7jDSTjCWNtLJutydG0+cwmqQtYSRp6BlLEhuj6TTgPGDXzHOkQTAeSRd3TXVf7jHSbDKWpAnKul3Apmh6beY5Uj8ykjRyjCXpRfSi6XRiNL0m8xypHxhJGlnGkpTQu7Hlp4FzgVdnniPlMAb8kHhNkpGkkWQsSZPQi6aPE0+b9sk8R5oLzwLXAUuNJI06Y0magrJuC+KH9J4OfBjYOu8iacY9DFwFLOua6rHcY6R+YCxJW6is292Jtx34BN52QIPv18SPBbqla6r1ucdI/cRYkqaprNttgGOJp03vyTxHmoq1xJfaruia6u7cY6R+ZSxJM6is2wOJ0XQi8IrMc6SXcjewDLi2a6r/5B4j9TtjSZoFZd0uBD5KDKf9Ms+RAJ4hvqvt211T3Z57jDRIjCVplpV1ewRwCvGC8IWZ52j03AEsB37gKZK0ZYwlaY6UdbstsBg4jhhOO+ZdpCF2O/Aj4MauqbrMW6SBZyxJGfTC6WhgCYaTpi8AfwBuJAbS3zLvkYaKsSRlVtbtdmwKpw9hOGlyAnAbm06Q/pF5jzS0jCWpj0wIp+OI4eQ76jRRAH5HDKSbuqZ6OPMeaSQYS1Kf6oXTB4j3cDoK2DXvImWyHvg98SW2m7qmWp15jzRyjCVpQJR1+2bgyN7jMGDnvIs0SzYAdwEre4/fdE31VN5J0mgzlqQBVNbtVsBbiOF0BHAoXus0qMaIN4lcCfwS+FXXVGuyLpL0AsaSNATKup0HvI0YTkcC7wa2zzpKLyUAf2bTydGtXVP9O+8kSSnGkjSEyrqdD7yTGE6HAm8FXpl11OjaAPwFuJVNcfRY3kmSpsJYkkZEWbdvAg4mhtP4Vy8an1mPAH+a8LgHWNU11bqsqyRNi7EkjbCybncDDgIOAPbvPfYDFuTcNQDWAauIMbQxjrqmeiTrKkmzwliS9AK9i8f35IXxtAewqPcYlWuhAvAosBr4O/E6o/E4ur9rqg0Zt0maQ8aSpCkp63YXYjTt3nss2uzr7sBO2QZOzrPECHqxx8O9r//smuq5bAsl9Q1jSdKMK+t2BzYF1CJgIfGlve17j6n+uQD++zKPdYnvPcWEIPIC6y1XFMW+wMUhhCW9/74GWBpCuHfC37kBOD+E8EBRFHsCXw4hnJBnsTR9xpIkaVKKoriZGL57A3f2nj4YeAB4PISwpCiK3YBrQghHFUWxhHjvqEtCCCdnmCzNCGNJkjRpRVHsBVwOXNB76iLgvBDCfb3vfw1YG0L4YlEUPwNOAK4H/ggsCCGckWG2NC1b5R4gSRo4OwJ79R4b7xxfFMUewPjLcwcST6CWE69hW4m3qtCAmpd7gCRp4OwDjJ8Q7bvZ80t7X08DHgJOAS4lvptw/RxulGaMJ0uSpJdVFMXWRVGM/874RQjh8BDC4cCK8e8TT4/uANaEEE4H1vb+/nxiMN0xt6ulmeHJkiRpMo4HTiLef4qiKFZM+N5lxP/5/i7w4GY/N6/3M6cCOxVFsV0IwTuaa6B4siRJelkhhGtDCIuBk4HngBOBD/b+/NUQwvtDCDds9mMLiDf2PDmE8CRwPnDU3K2WZoYnS5KkSSmK4ljgbODMEMJjvedOBa4uimKfEMIy4u+V+b0fWUl8ma4oigLiDT9X/N8/LPU5bx0gSZqUoih2BtaFENZu9vw8YGEI4Yk8y6TZZSxJkiQleM2SJElSgrEkSZKUYCxJkiQlGEuSJEkJxpIkSVKCsSRJkpRgLEmSJCUYS5IkSQnGkiRJUoKxJEmSlGAsSZIkJRhLkiRJCcaSJElSgrEkSZKUYCxJkiQlGEuSJEkJxpIkSVKCsSRJkpRgLEmSJCUYS5IkSQnGkiRJUoKxJEmSlGAsSZIkJRhLkiRJCcaSJElSgrEkSZKUYCxJkiQlGEuSJEkJxpIkSVKCsSRJkpRgLEmSJCUYS5IkSQnGkiRJUoKxJEmSlGAsSZIkJRhLkiRJCcaSJElSgrEkSZKUYCxJkiQlGEuSJEkJxpIkSVKCsSRJkpRgLEmSJCUYS5IkSQnGkiRJUoKxJEmSlGAsSZIkJRhLkiRJCcaSJElSgrEkSZKUYCxJkiQlGEuSJEkJxpIkSVKCsSRJkpRgLEmSJCX8D/FlaIg+AzzkAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "car_top10 = pd.value_counts(car[\"Brand\"]).sort_values(ascending= False)[0:10]\n",
    "\n",
    "brand = []\n",
    "brand_num = []\n",
    "for index in car_top10.index:\n",
    "    brand.append(index)\n",
    "for value in car_top10.values:\n",
    "    brand_num.append(value)\n",
    "    \n",
    "brand.append(\"其他\")\n",
    "brand_num.append(car.shape[0]-sum(brand_num))\n",
    "\n",
    "data = dict(zip(brand,brand_num))\n",
    "df = pd.Series(data)\n",
    "\n",
    "df.plot(kind=\"pie\",\n",
    "       figsize=(10,10),\n",
    "       title = \"车辆销售品牌占比\",\n",
    "       autopct = \"%.2f\" )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 5、排放标准分析"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x27a2ce4cda0>"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "discharge = car[\"Discharge\"].value_counts()\n",
    "discharge.plot(kind = \"bar\",figsize=(12,8))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 6、车龄分析 "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x27a2e98a240>"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "car_year = car[\"year\"].value_counts()\n",
    "car_year = car_year.to_frame().sort_index()\n",
    "car_year.plot(kind=\"bar\",figsize=(12,8))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 7、里程分析"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x27a2ebdd630>"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "kmBins = [0,3,6,9,12,15,1000000]\n",
    "kmlabels = [\"0_3\",\"4_6\",\"7_9\",\"10_12\",\"13_15\",\"16+\"]\n",
    "\n",
    "km = pd.cut(car[\"Km\"],bins = kmBins,labels = kmlabels,include_lowest = True)\n",
    "km.value_counts().to_frame().sort_index().plot(kind=\"bar\",figsize=(12,8))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 8、折旧价格分析"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x27a2e644fd0>"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "car[\"zhejiu\"] = car.apply(lambda x :x[\"Sec_price\"]/x[\"New_price\"],axis = 1)\n",
    "zjBins = [0,0.2,0.4,0.6,0.8,1]\n",
    "zjlabels = [\"20%-\",\"20%40%\",\"40%-60%\",\"60%-80%\",\"80%+\"]\n",
    "zhejiu = pd.cut(car[\"zhejiu\"],bins = zjBins,labels = zjlabels,include_lowest = True)\n",
    "zhejiu.value_counts().to_frame().sort_index().plot(kind = \"bar\",figsize=(12,8))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 9、行驶公里数与价格"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x27a2f0a5240>"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "index = car[\"Brand\"].isin([\"奥迪\"])\n",
    "some_cars = car.loc[index,:]\n",
    "\n",
    "some_cars.plot.scatter(figsize=(12,8),\n",
    "                      x = \"Km\",\n",
    "                      y =\"Sec_price\",\n",
    "                      s = 20,\n",
    "                      marker = \"o\",\n",
    "                      alpha = 0.9,\n",
    "                      linewidths = 0.3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
